Computing cloud and method for a cloud source for monitoring at least one piece of lifting equipment

Abstract

Provided is a method (100) for a cloud source for monitoring at least one piece of lifting equipment. The method includes receiving operational data (102) of the at least one piece of lifting equipment. Additionally, the method includes determining (104) at least one quantity characterizing the at least one piece of lifting equipment based on the operational data.

Description

Field

[0001] The present disclosure relates to monitoring of lifting equipment. In particular, examples of the present disclosure relate to a computing cloud and a method for a cloud source for monitoring at least one piece of lifting equipment.Background

[0002] Cranes, in particular loader cranes play a pivotal role in various industries, providing efficient lifting and handling capabilities. However, operators or owners of cranes have no or only very limited technical insights on the operation of the cranes. This unnecessarily complicates a large number of tasks, such as crane maintenance. The same problem arises with other types of lifting equipment equipped with a hydraulic system, such as forklifts or lifting platforms.

[0003] Hence, there may be a demand for improved monitoring of lifting equipment.Summary

[0004] This demand is met by a method for a cloud source for monitoring at least one piece of lifting equipment, a computing cloud, a non-transitory machine-readable medium and a program in accordance with the independent claims. Advantageous embodiments are defined by the dependent claims.

[0005] According to a first aspect, the present disclosure provides a method for a cloud source for monitoring at least one piece of lifting equipment. The method comprises receiving operational data of the at least one piece of lifting equipment. Additionally, the method comprises determining at least one quantity characterizing the at least one piece of lifting equipment based on the operational data.

[0006] According to a second aspect, the present disclosure provides a computing cloud comprising at least interface circuitry and processing circuitry configured to perform the method according to the first aspect.

[0007] According to a third aspect, the present disclosure provides a non-transitory machine-readable medium having stored thereon a program having a program code for performing the method according to the first aspect, when the program is executed on a processor or a programmable hardware.

[0008] According to a fourth aspect, the present disclosure provides a program having a program code for performing the method according to the first aspect, when the program is executed on a processor or a programmable hardware.

[0009] The proposed technology allows for remote monitoring and management of lifting equipment. This enables centralized oversight from any location with internet access. Furthermore, real-time data processing and analysis is enabled. Accordingly, operational data from lifting equipment can be analyzed instantly, leading to timely decision-making and quicker response to potential issues. Furthermore, various applications such as predictive maintenance are enabled, thus reducing downtime and improving equipment lifespan. Monitoring operational data and determining key quantities may further enhance safety. It is possible to identify potentially problematic operating conditions, detect anomalies, and provide alerts or automatic shutdowns to prevent accidents. Furthermore, optimization of equipment usage may be enabled by analyzing usage patterns and operational efficiency. This can lead to better scheduling, reduced energy consumption, and overall improved performance of the lifting equipment. Accordingly, the sustainability of the lifting equipment may be improved. The cloud-based technology is inherently scalable allowing to accommodate more data and more complex analysis without significant changes to the infrastructure as the amount of lifting equipment grows. Overall, the proposed technology leverages cloud technology to provide a comprehensive, efficient, and scalable solution for monitoring and managing lifting equipment, enhancing various aspects such as operational efficiency, safety, and maintenance practices.Brief description of the Figures

[0010] Some examples of apparatuses and / or methods will be described in the following by way of example only, and with reference to the accompanying figures, in which Fig. 1 illustrates a flowchart of an example of a method for a cloud source for monitoring at least one piece of lifting equipment; and Fig. 2 illustrates an exemplary computing cloud. Detailed Description

[0011] Some examples are now described in more detail with reference to the enclosed figures. However, other possible examples are not limited to the features of these embodiments described in detail. Other examples may include modifications of the features as well as equivalents and alternatives to the features. Furthermore, the terminology used herein to describe certain examples should not be restrictive of further possible examples.

[0012] Throughout the description of the figures same or similar reference numerals refer to same or similar elements and / or features, which may be identical or implemented in a modified form while providing the same or a similar function. The thickness of lines, layers and / or areas in the figures may also be exaggerated for clarification.

[0013] When two elements A and B are combined using an "or", this is to be understood as disclosing all possible combinations, i.e. only A, only B as well as A and B, unless expressly defined otherwise in the individual case. As an alternative wording for the same combinations, "at least one of A and B" or "A and / or B" may be used. This applies equivalently to combinations of more than two elements.

[0014] If a singular form, such as "a", "an" and "the" is used and the use of only a single element is not defined as mandatory either explicitly or implicitly, further examples may also use several elements to implement the same function. If a function is described below as implemented using multiple elements, further examples may implement the same function using a single element or a single processing entity. It is further understood that the terms "include", "including", "comprise" and / or "comprising", when used, describe the presence of the specified features, integers, steps, operations, processes, elements, components and / or a group thereof, but do not exclude the presence or addition of one or more other features, integers, steps, operations, processes, elements, components and / or a group thereof.

[0015] Fig. 1 illustrates a method 100 for (performed by) a cloud source for monitoring at least one piece of lifting equipment.

[0016] A cloud service, as used in the present disclosure, is a remotely accessible computing resource or set of resources delivered over a network, such as the internet. These resources may, e.g., encompass computing power, storage, databases, software applications and networking capabilities. For example, a computing cloud or a data center may host (run) the cloud service.

[0017] A piece of lifting equipment is any machinery (device) for lifting loads (e.g., goods and / or people) using hydraulic power. For example, the lifting equipment may be a crane such as a knuckle boom or loader crane for loading and unloading goods (loads). Alternatively, the lifting equipment may, e.g., be the crane section of a mobile crane (with a vehicle being a mobile platform having mounted thereon the crane section) or the entire mobile crane. In other examples, the lifting equipment may, e.g., be a forklift comprising a pronged device in the front, called forks, which can be inserted under loads to lift and carry them. In still other examples, the lifting equipment may, e.g., be a lifting platform (elevating platform of lift table) comprising a platform supported by a mechanical structure in a liftable manner to a base of the lifting platform.

[0018] The method 100 comprises receiving 102 operational data of the at least one piece of lifting equipment. The operational data refers to various types of information that describe the performance, status, and conditions under which the respective lifting equipment operates. For example, the operational data may indicate the weight of load being lifted, the duration of operation or usage patterns, the current (operational) status such as a current operation mode (e.g., active, idle, power mode, eco-mode, etc.), performance metrics such as lift speed or energy consumption. However, it is to be noted that the present disclosure is not limited to the foregoing examples. The operational data may indicate more, less or different pieces of information. The operational data may relate to (originate from) a single piece of lifting equipment like a single crane or to (from) multiple (i.e., two or more) pieces of lifting equipment like two or more cranes. The operational data may be received directly from the respective lifting equipment or alternatively from an intermediate device such as a remote server (e.g., of an operator or owner of the respective lifting equipment) communicatively coupled between the respective lifting equipment and the cloud service. The operational data may, e.g., be received continuously and / or aggregated.

[0019] Additionally, the method 100 comprises determining 104 at least one quantity characterizing the at least one piece of lifting equipment based on the operational data. In other words, the operational data are analyzed to determine (e.g., calculate) one or more specific quantities that describe or characterize the equipment's performance, condition, or usage. The one or more determined quantities provide insight into specific attributes or performance metrics of the lifting equipment. These quantities may help in understanding various aspects of the respective lifting equipment such as its functioning, efficiency, etc. Various exemplary quantities will be described below. However, it is to be noted that the present disclosure is not limited to these specific examples. The at least one quantity may be determined based on at least one of real-time operational data (i.e., operational data that is delivered / received immediately after collection / generation without significant delay) and historical operational data (i.e., operational data collected over a period of time in the past). For example, a computational model may be used for determining the at least one quantity. The computational model is a data analysis model for deriving the at least one quantity from the input operational data. The computational model may, e.g., be a mechanistic (i.e., rule-based) model, a trained machine-learning model or a combination thereof. The computational model may use various signal and / or data processing operations such as signal / data addition, subtraction, multiplication, division, integration, derivation, filtering (e.g., discrete, continuous or both), delaying, etc. to determine the at least one quantity from the input operational data.

[0020] The method 100 allows for remote monitoring and management of lifting equipment. This enables centralized oversight from any location with internet access. Furthermore, real-time data processing and analysis is enabled. Accordingly, operational data from lifting equipment can be analyzed instantly, leading to timely decision-making and quicker response to potential issues. Furthermore, various applications such as predictive maintenance are enabled, thus reducing downtime and improving equipment lifespan. Monitoring operational data and determining key quantities may further enhance safety. It is possible to identify potentially problematic operating conditions, detect anomalies, and provide alerts or automatic shutdowns to prevent accidents. Furthermore, optimization of equipment usage may be enabled by analyzing usage patterns and operational efficiency. This can lead to better scheduling, reduced energy consumption, and overall improved performance of the lifting equipment. Accordingly, the sustainability of the lifting equipment may be improved. The cloud-based technology is inherently scalable allowing to accommodate more data and more complex analysis without significant changes to the infrastructure as the amount of lifting equipment grows. Overall, the method 100 leverages cloud technology to provide a comprehensive, efficient, and scalable solution for monitoring and managing lifting equipment, enhancing various aspects such as operational efficiency, safety, and maintenance practices.

[0021] According to examples of the present disclosure, the method 100 may further comprise providing (making available) 106 data about the at least one quantity for retrieval by an application executed on a terminal device of a user. For example, the data about the at least one quantity may be stored in a cloud storage or another storage system accessible by the application. The provisioning of the data about the at least one quantity for retrieval by the application enables users to easily access and interact with the data from their terminal devices, enhancing usability and convenience. The terminal device of the user may, e.g., be a mobile terminal device such as a mobile phone, a tablet-computer, or a laptop computer, or a stationary terminal device such as a computer. The application may, e.g., be an application specifically provided for monitoring the at least one piece of lifting equipment. Alternatively, the application can also be a universal application (e.g., an Internet browser). The information about the at least one quantity characterizing the at least one piece of lifting equipment may, e.g., be provided as a value or series of values retrievable via the application or as a graphic retrievable via the application. Accordingly, the information about the at least one quantity characterizing the at least one piece of lifting equipment may be provided in a format that is easy to understand for the user. For example, the application may offer features for visualizing and analyzing the data, helping users to understand trends, detect anomalies, and make informed decisions.

[0022] In some examples of the present disclosure, the data about the at least one quantity characterizing the at least one piece of lifting equipment may be provided on a website with access restricted to a predetermined user group. The provision of the information about the at least one quantity characterizing the at least one piece of lifting equipment on a web site enables access at any time and place to the at least one quantity characterizing the at least one piece of lifting equipment by the user, and thus a flexible monitoring of the at least one piece of lifting equipment. The output of the information via a web site may further enable the user to create individual notifications using appropriate configuration options on the web site. In addition, the access restriction enables the monitoring to be secured against access by unauthorized third parties. Access can be restricted, for example, by a password, a security certificate, or local access restrictions.

[0023] The data about the at least one quantity characterizing the at least one piece of lifting equipment may optionally be stored in a cloud storage or another storage system. Consequently, the information about the at least one quantity characterizing the at least one piece of lifting equipment may be accessed flexibly in terms of time and location, with a minimized risk of data loss.

[0024] According to examples of the present disclosure, determining 104 the at least one quantity characterizing the at least one piece of lifting equipment may be carried out continuously. In other words, the process of determining the at least one quantity characterizing the at least one piece of lifting equipment may be performed on an ongoing, uninterrupted basis. This means that the incoming operational data is constantly analyzed to update the one or more quantities in, e.g., real-time or near real-time. Continuous determination allows for real-time monitoring of the at least one piece of lifting equipment. A user may be permanently provided with current values for the at least one characterizing quantity for retrieval. In addition, the continuous determination of the at least one quantity characterizing quantity enables determining temporal courses or temporal changes of the at least one characterizing quantity, which in turn may themselves characterize the at least one piece of lifting equipment or the operating behavior thereof.

[0025] In alternative examples of the present disclosure, the at least one quantity characterizing the at least one piece of lifting equipment may be determined discontinuously - for example, periodically or as a result of the occurrence or detection of the occurrence of a predetermined event. If, for example, a specific quantity characterizing the at least one piece of lifting equipment is required only once a year to prepare a legally required report, it may be sufficient to determine this quantity only once a year from the system data. In this way, calculation effort can be saved for unnecessary determinations (e.g. calculations) of the at least one quantity characterizing the at least one piece of lifting equipment.

[0026] In some examples of the present disclosure, the at least one quantity may be determined for operational data for (of) at least two different operating periods of the at least one lifting equipment. In other words, the method 100 may comprise determining 104 the at least one quantity separately for at least two distinct time periods during which the at least one piece of lifting equipment is operating. These periods may, e.g., be hours of the day, days, weeks, months, or any defined intervals of operation. In these examples, the method 100 further comprises comparing 108 the determined at least one quantities for the at least two different operating periods. In other words, after determining the at least one quantity for the different operating periods, the method 100 comprises comparing these quantities to, e.g., analyze changes, trends, or patterns over time. The comparison may enable identification of trends and patterns in the lifting equipment's performance and usage, which can be critical for long-term planning and decision-making. Furthermore, the comparison may allow for benchmarking performance across different periods, helping to assess the impact of changes in operations, maintenance practices, or environmental conditions. The comparison may further provide insights into how the condition of the lifting equipment evolves over time, supporting maintenance planning and lifecycle management.

[0027] For example, an owner or operator of a piece of lifting equipment such as a crane may use the method to continuously collect the operational data over a span of several months. One or more quantities characterizing the piece of lifting equipment is determined for each month. The determined quantities are then compared month-over-month. The comparison may reveal various aspects. For example, the comparison may reveal that the piece of lifting equipment has a declining efficiency trend, prompting a closer inspection and early maintenance intervention. In other examples, the comparison may reveal peak usage periods which may help in resource planning and allocation.

[0028] In some examples of the present disclosure, the at least one quantity may be determined for operational data of at least two pieces of lifting equipment. In other words, the method 100 may comprise determining 104 the at least one quantity separately for at least two different pieces of lifting equipment (e.g., two or more cranes). In these examples, the method 100 further comprises comparing 110 the determined at least one quantities for the at least two pieces of lifting equipment. In other words, after determining the at least one quantity for the different pieces of equipment, the method 100 comprises comparing 110 these quantities to, e.g., analyze performance, efficiency, or condition across multiple lifting equipment units. The comparison may allow for benchmarking performance across different lifting equipment units, e.g., helping to identify which equipment is performing better or worse. Furthermore, the identification of operational inefficiencies may be facilitated by comparing metrics across multiple lifting equipment units, leading to improved overall efficiency.

[0029] For example, an owner or operator of a piece of lifting equipment such as a crane may use the method to simultaneously collect the operational data of two pieces of lifting equipment used for the same purpose (e.g., loading goods onto a truck at a site A and unloading the goods from the truck at a site B). One or more quantities characterizing the respective lifting equipment are determined for each lifting equipment and compared to each other. The comparison may reveal various aspects. For example, the comparison may reveal that the pieces of lifting equipment show different performances, helping to identify underperforming units and potential areas for improvement. In other examples, the comparison may reveal varying maintenance needs of different equipment, supporting more targeted and effective maintenance strategies.

[0030] The determined at least one quantity may be used for predictive maintenance according to examples of the present disclosure. Accordingly, the method 100 may optionally further comprise predicting 112 failure of a component of the at least one piece of lifting equipment based on the determined at least one quantity. For example, a failure model may be used for predicting failure of the component of the at least one piece of lifting equipment. The failure model is a data analysis model for predicting failure of the component of the at least one piece of lifting equipment from the input at least one quantity and optionally further data. The failure model may, e.g., be a mechanistic (i.e., rule-based) model, a trained machine-learning model or a combination thereof. The failure model is configured to predict when a component of a lifting equipment is likely to fail, based on the analysis of the determined one or more quantities. This prediction may include estimating the time frame or operational cycles remaining before failure. Predictive maintenance enables maintenance to be performed proactively based on predicted failures, reducing the risk of unexpected breakdowns and extending the lifespan of the lifting equipment. By predicting failures before they occur, the method helps minimize unplanned downtime, keeping the lifting equipment operational and productive. Preventive maintenance based on accurate predictions can lead to significant cost savings by avoiding expensive emergency repairs and extending component life. This may further improve the sustainability of the lifting equipment. Predicting potential failures may enhance safety as potential issues may be addressed before they become serious.

[0031] Optionally, the method 100 may further comprise performing a predefined action based on the predicted failure of the component of the at least one piece of lifting equipment. The predefined action may be manifold. For example, the predefined action may be one or more of causing output of a notification (e.g., a message or an e-mail) about the failure on a terminal device of a user (e.g., a mobile phone or a tablet-computer of a user) and causing output of an alarm (e.g., activating of an audio alarm and / or a visual alarm). Accordingly, one or more user or people may be informed about the failure. Alternatively or additionally, the predefined action may further be one or more of causing the at least one piece of lifting equipment to change its operation (e.g., to immediately shut down). Accordingly, a potentially serious situation caused by the predicted failure may be avoided and, hence, safety be enhanced.

[0032] For example, an owner or operator of a piece of lifting equipment such as a crane may use the method to collect the operational data of the lifting equipment. Using the operational data, the system may, e.g., predict that a component such as a bearing, a valve or a hydraulic cylinder is likely to fail within the next 100 operational hours. Accordingly, a maintenance team may be alerted, who schedule a preventive replacement of the component (e.g., outside planned operating hours of the lifting equipment), thus avoiding failure of the component that could disrupt operations and incur significant costs.

[0033] As described above, the at least one quantity may be manifold. In the following, some examples will be described in greater. However, it is to be noted that the present disclosure is not limited to these specific examples.

[0034] In some examples of the present disclosure, the at least one quantity is a consumed energy per operation of the at least one piece of lifting equipment. In other words, the amount of energy consumed by the lifting equipment for each operation may be determined according to the method 100. The operation could refer to a single lift, a cycle of lifting and lowering, or any defined unit of activity performed by the at least one piece of lifting equipment.

[0035] For example, the operational data may indicate a start time t s and an end time t e for an operation as well as the power consumption P(t) of the lifting equipment over time. Accordingly, the consumed energy for an operation may be determined as follows: E = ∫ t s t e P t dt

[0036] In alternative example, the power consumption may be derived from the operational data. Various functions of lifting equipment such as lifting, lowering, extending, retracting, rotating, and stabilizing use hydraulic power provided by a displacement pump of a hydraulic system of a lifting equipment. Accordingly, the power consumption may be mainly determined by the power used for operating the hydraulic system. The displacement pump of the lifting equipment is driven by a rotary drive (e.g., a Power Take-Off, PTO, or an electric rotary drive, also known as electric Power Take-Off, ePTO) to provide hydraulic power. For example, the power consumption may be derived from the rotational speed of the rotary drive driving the displacement pump of the lifting equipment's hydraulic system. The operational data may indicate the rotational speed. Similarly, the operational data may indicate a torque provided by the rotary drive and the power consumption may be derived from the torque. The start time t s and the end time t e for an operation may, e.g., be defined by the activation (engagement) period or time frame of the rotary drive.

[0037] Monitoring the consumed energy per operation may enables monitoring of the energy efficiency of the lifting equipment, helping to identify inefficient operations and opportunities for energy savings. By understanding energy consumption patterns, operators or owners may manage and reduce energy costs, leading to significant savings over time. Analyzing energy consumption per operation may help in optimizing the performance of the lifting equipment by identifying and addressing factors that contribute to excessive energy use. Reducing energy consumption also reduces the environmental impact of the lifting equipment, promoting sustainability. Changes in energy consumption patterns can be an indicator of potential issues or wear in the lifting equipment, supporting predictive maintenance strategies. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the consumed energy per operation.

[0038] Analogously to the consumed energy per operation, the at least one quantity may be a consumed energy per operation day of the at least one piece of lifting equipment. In other words, the amount of energy consumed by the lifting equipment over the course of an entire operation day may be determined according to the method 100. The operation day may refer to a 24-hour period or the specific working hours in which the lifting equipment is used during a day. For example, the consumed amounts of energy for all operations during the operation day may be summed up. This enables monitoring of the energy efficiency of the lifting equipment or other related parameters on a daily basis rather than on a per operation basis.

[0039] Analogously to the consumed energy per operation and the consumed energy per operation day, the at least one quantity may be a consumed energy over the (entire) lifetime of the at least one piece of lifting equipment. In other words, the total amount of energy consumed by the lifting equipment over its entire operational lifetime may be determined according to the method 100. The lifetime refers to the period from the initial use of the lifting equipment to the present (or until the lifting equipment is decommissioned or replaced). For example, the consumed amounts of energy for all operations during the lifetime may be summed up. This enables monitoring of the energy efficiency of the lifting equipment or other related parameters over its entire operational lifetime, helping to identify long-term inefficiencies and opportunities for improvement.

[0040] In some examples, the at least one quantity may concern the hydraulic system of the lifting equipment. For example, the at least one quantity may be a number of pump circulations of the displacement pump of the hydraulic system of the at least one piece of lifting equipment. In other words, the count of pump circulations or cycles of the displacement pump within the hydraulic system of the lifting equipment may be determined. A circulation or cycle refers to one complete operational cycle of the displacement pump, from intake to discharge. The number of pump circulations may be determined per operation, per operation day or over the (entire) lifetime of the at least one piece of lifting equipment. For example, the number of pump circulations may be derived from the operating periods of the rotary drive driving the displacement pump and the rotational speed of the rotary drive. The operational data may indicate the rotational speed. The operating periods of the rotary drive may, e.g., be defined by the activation (engagement) period or time frame of the rotary drive. Accordingly, placement of a dedicated sensor for measuring the number of pump circulations may avoided.

[0041] However, in alternative examples, the pump circulations measured by a dedicated senor may be integrated for the operating periods of the rotary drive to determine the number of pump circulations. The number of pump circulations may enable detailed monitoring of the hydraulic system's performance by tracking the activity of the displacement pump, a critical component in the system. By monitoring the number of pump circulations, the method 100 may help predict when the displacement pump or other hydraulic components may require maintenance or replacement, reducing the risk of unexpected failures. The number of pump circulations may provide insights into the wear and tear on the displacement pump, supporting better management of the component's lifespan and planning for timely maintenance. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the number of pump circulations. Analyzing the pump circulation data may further reveal inefficiencies or overuse in the hydraulic system, guiding optimization efforts to improve overall operational efficiency.

[0042] In some examples, the at least one quantity is a predefined number of maximum pump speeds of the displacement pump of the hydraulic system of the at least one piece of lifting equipment. In other words, a predefined number of the highest pump speeds achieved (driven) by the displacement pump during operation may be determined according to the method 100 (e.g., the five, ten, 15 or 20 highest pump speeds). Only pump speeds that were achieved (driven) by the displacement pump for at least a predefined time period (e.g., 1 second, 2 seconds, 5 seconds, 10 seconds, 30 seconds or more) may be determined. For example, the pump speeds may be derived from the rotational speed of the rotary drive. However, in alternative examples, the pump speeds may be measured by a dedicated senor.

[0043] The maximum pump speeds achieved by the displacement pump may enable detailed monitoring of the displacement pump's performance by tracking instances of maximum speed operation, which can indicate high-demand periods or potential stress on the system. Analyzing the data on maximum pump speeds can reveal inefficiencies or overuse in the hydraulic system, guiding optimization efforts to improve overall operational efficiency. Furthermore, the maximum pump speeds provide insights into the wear and tear on the displacement pump, supporting better management of the component's lifespan and planning for timely maintenance. By monitoring the maximum pump speeds, the method 100 may help predict when the pump or other hydraulic components may require maintenance or replacement, reducing the risk of unexpected failures. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the predefined number of maximum pump speeds of the displacement pump.

[0044] In some examples, the at least one quantity may be a predefined number of maximum temperatures measured in the hydraulic system of the at least one piece of lifting equipment. In other words, a predefined number of the highest temperatures measured in the hydraulic system may be determined according to the method 100 (e.g., the five, ten, 15 or 20 highest temperatures). Only temperatures that were measured in the hydraulic system for at least a predefined time period (e.g., 1 second, 2 seconds, 5 seconds, 10 seconds, 30 seconds or more) may be determined. For example, the temperatures may be measured by a dedicated senor. Temperature is an important parameter as it can indicate the health and performance of the hydraulic system. The measured temperatures provides insights into the wear and tear on the hydraulic system, supporting better management of the components' lifespan and planning for timely maintenance. By monitoring the maximum temperatures, the method 100 may help predict when the hydraulic system or its components may require maintenance or replacement, reducing the risk of unexpected failures. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the predefined number of maximum temperatures measured in the hydraulic system

[0045] According to some examples, the at least one quantity is a moved volume of hydraulic fluid in the hydraulic system of the at least one piece of lifting equipment. In other words, the volume of hydraulic fluid moved within the hydraulic system may be determined according to the method 100. The moved volume of hydraulic fluid may be determined per operation, per operation day or over the (entire) lifetime of the at least one piece of lifting equipment. In general, any type of hydraulic fluid suitable for transmitting power efficiently may be used in the hydraulic system. For example, the hydraulic fluid may be a mineral oil-based hydraulic fluid or a synthetic hydraulic fluid. The volume of hydraulic fluid moved is an indicator of the hydraulic system's activity and performance. For example, the volume of hydraulic fluid moved within the hydraulic system may be derived from the operating periods of the rotary drive driving the displacement pump and the rotational speed of the rotary drive. The operational data may indicate the rotational speed. The operating periods of the rotary drive may, e.g., be defined by the activation (engagement) period or time frame of the rotary drive. Further parameters such as a load sensing signal of the displacement pump or the swivel angle of the displacement pumps swash plate may be indicated by the operational data and be taken into account for the determination of the volume of hydraulic fluid moved within the hydraulic system. Accordingly, placement of a dedicated sensor for measuring the volume of hydraulic fluid moved within the hydraulic system may avoided. However, in alternative examples, the volume of hydraulic fluid moved within the hydraulic system measured by a dedicated senor may be integrated for the operating periods of the rotary drive to determine the total volume of hydraulic fluid moved within the hydraulic system. Tracking the volume of hydraulic fluid moved may enables detailed monitoring of the hydraulic system's performance, which is crucial for understanding hydraulic system activity and efficiency. The moved volume of hydraulic fluid in the hydraulic system may provide insights into the wear and tear on the hydraulic system, supporting better management of the components' lifespan and planning for timely maintenance. By monitoring the volume of hydraulic fluid moved, the method 100 may help predict when components of the hydraulic system may require maintenance or replacement (e.g., replacement of the hydraulic fluid or filters), reducing the risk of unexpected failures. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the moved volume of hydraulic fluid in the hydraulic system.

[0046] In some examples, determining 104 the at least one quantity may comprise determining, based on the operational data, a temporal progression of the rotational speed of the rotary drive driving the displacement pump of the hydraulic system of the at least one piece of lifting equipment. In other words, it is determined how the rotational speed of the rotary drive changes over time. For example, the operational data may indicate the torque and the rotational speed of the rotary device and these pieces of information may be analyzed to determine the temporal progression of the rotary drive's rotational speed. In these examples, determining 104 the at least one quantity may further comprise determining whether an anomaly occurs in the temporal progression of the rotational speed. The anomaly is an unusual or unexpected pattern in the temporal progression of the rotational speed. The anomaly may be anything that deviates from normal or expected patterns. In other words, the temporal progression of the rotary drive's rotational speed is analyzed to identify deviations from normal or expected behavior. Anomalies may, e.g., include sudden spikes or drops in speed, unusual fluctuations, or patterns that do not match historical data (e.g., oscillations). Anomalies may indicate potential issues such as mechanical problems, inefficiencies, or impending failures. The at least one quantity indicates whether an anomaly occurs in the temporal progression of the rotational speed. In other words, the at least one quantity is an indication of the presence or absence of anomalies. For example, an anomaly detection model may be used for determining whether an anomaly occurs in the temporal progression of the rotational speed. The anomaly detection model is a data analysis model for determining whether an anomaly occurs in the temporal progression of the rotational speed from the input temporal progression of the rotary drive's rotational speed and optionally further data. The anomaly detection model may, e.g., be a mechanistic (i.e., rule-based) model, a trained machine-learning model or a combination thereof. The anomaly detection model may configured to identify anomalies based on predefined criteria, historical data or trained knowledge.

[0047] By monitoring and analyzing the temporal progression of the rotational speed, the method 100 may detect early signs of mechanical issues or inefficiencies, allowing for timely maintenance and preventing unexpected failures. For example, swinging and vibrations within the hydraulic system may be detected, helping in the predictive maintenance of the hydraulic system. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the detected occurrence of an anomaly.

[0048] According to some examples, determining 104 the at least one quantity may comprise determining, based on the operational data, a respective pattern of power consumed by the rotary drive driving the displacement pump of the hydraulic system of the at least one piece of lifting equipment for at least one type of movement of the at least one piece of lifting equipment. In other words, the power consumption profile(s) for one or more specific types of movements performed by the lifting equipment (e.g., lifting, lowering, rotating, extending) are determined. For example, the operational data may indicate the torque and the rotational speed of the rotary device and these pieces of information may be analyzed to determine the patterns of consumed power. In these examples, determining 104 the at least one quantity may further comprise determining, based on the operational data, whether the respective pattern changes over time. In other words, it is determined how the power consumption pattern evolves or deviates over time. For example, the power consumption patterns may be continuously monitored and compared to established profiles. Changes in the pattern are detected by identifying deviations from the expected power consumption. For example, it may be detected that rotational speed of the rotary drives decreased for the same consumed power and load that is being lifted. The at least one quantity indicates whether the respective pattern changes over time. In other words, the at least one quantity is an indication of the presence or absence of changes in power consumption patterns.

[0049] By monitoring changes in power consumption patterns, the method 100 may detect early signs of mechanical issues or inefficiencies, allowing for timely maintenance and preventing unexpected failures. For example, increasing friction in the hydraulic system may cause an increased power consumption for the same operation over time. Detecting changes in the power consumption may help in the predictive maintenance of the hydraulic system. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the detection of one or more patterns changing over time.

[0050] Fig. 2 further illustrates an exemplary computing cloud or data center 200 for performing the method 100 described above.

[0051] The computing cloud 200 comprises at least interface circuitry 210 and processing circuitry 220. The interface circuitry 210 and the processing circuitry 220 are configured to perform the method 100 described above.

[0052] The interface circuitry 210 is configured for interfacing and data exchange with external entities. For example, the interface circuitry 210 may be configured to receive the operational data of the at least one piece of lifting equipment directly from the respective lifting equipment or alternatively from an intermediate device such as a remote server of an operator or owner of the respective lifting equipment. The interface circuitry 210 may be configured for wireless and / or wired communication with the external entities.

[0053] The processing circuitry 220 is communicatively coupled to the interface circuitry 210. For example, the processing circuitry 210 may be a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which or all of which may be shared, a digital signal processor (DSP) hardware, an application specific integrated circuit (ASIC), a system-on-a-chip (SOC), a neuromorphic processor or a field programmable gate array (FPGA). The computing cloud 200 may comprise memory configured to store instructions, which when executed by the processing circuitry 220, cause the processing circuitry 220 and the interface circuitry 210 to perform the method 100.

[0054] Analogously to what is described above for the method 100, the computing cloud 200 may allow for remote monitoring and management of lifting equipment. The computing cloud 200 may allow to provide a comprehensive, efficient, and scalable solution for monitoring and managing lifting equipment, enhancing various aspects such as operational efficiency, safety, sustainability and maintenance practices.

[0055] The examples described herein may be summarized as follows: An example (e.g., example 1) relates to a method for a cloud source for monitoring at least one piece of lifting equipment. The method comprises receiving operational data of the at least one piece of lifting equipment. Additionally, the method comprises determining at least one quantity characterizing the at least one piece of lifting equipment based on the operational data.

[0056] Another example (e.g., example 2) relates to a previous example (e.g., example 1) or to any other example, further comprising providing data about the at least one quantity for retrieval by an application executed on a terminal device of a user.

[0057] Another example (e.g., example 3) relates to a previous example (e.g., example 2) or to any other example, wherein the data about the at least one quantity are provided on a website with access restricted to a predetermined user group.

[0058] Another example (e.g., example 4) relates to a previous example (e.g., one of the examples 1 to 3) or to any other example, wherein determining the at least one quantity is carried out continuously.

[0059] Another example (e.g., example 5) relates to a previous example (e.g., one of the examples 1 to 4) or to any other example, wherein the at least one quantity is determined for operational data for at least two different operating periods of the at least one lifting equipment, and wherein the method further comprises comparing the determined at least one quantities for the at least two different operating periods.

[0060] Another example (e.g., example 6) relates to a previous example (e.g., one of the examples 1 to 5) or to any other example, wherein the at least one quantity is determined for operational data of at least two pieces of lifting equipment, and wherein the method further comprises comparing the determined at least one quantities for the at least two pieces of lifting equipment.

[0061] Another example (e.g., example 7) relates to a previous example (e.g., one of the examples 1 to 6) or to any other example, further comprising predicting failure of a component of the at least one piece of lifting equipment based on the at least one quantity.

[0062] Another example (e.g., example 8) relates to a previous example (e.g., one of the examples 1 to 7) or to any other example, wherein the at least one quantity is a consumed energy per operation of the at least one piece of lifting equipment.

[0063] Another example (e.g., example 9) relates to a previous example (e.g., one of the examples 1 to 8) or to any other example, wherein the at least one quantity is a consumed energy per operation day of the at least one piece of lifting equipment.

[0064] Another example (e.g., example 10) relates to a previous example (e.g., one of the examples 1 to 9) or to any other example, wherein the at least one quantity is a consumed energy over the lifetime of the at least one piece of lifting equipment.

[0065] Another example (e.g., example 11) relates to a previous example (e.g., one of the examples 1 to 10) or to any other example, wherein the at least one quantity is a number of pump circulations of a displacement pump of a hydraulic system of the at least one piece of lifting equipment.

[0066] Another example (e.g., example 12) relates to a previous example (e.g., one of the examples 1 to 11) or to any other example, wherein the at least one quantity is a predefined number of maximum pump speeds of a displacement pump of a hydraulic system of the at least one piece of lifting equipment.

[0067] Another example (e.g., example 13) relates to a previous example (e.g., one of the examples 1 to 12) or to any other example, wherein the at least one quantity is a predefined number of maximum temperatures measured in a hydraulic system of the at least one piece of lifting equipment.

[0068] Another example (e.g., example 14) relates to a previous example (e.g., one of the examples 1 to 13) or to any other example, wherein the at least one quantity is a moved volume of hydraulic fluid in a hydraulic system of the at least one piece of lifting equipment.

[0069] Another example (e.g., example 15) relates to a previous example (e.g., one of the examples 1 to 14) or to any other example, wherein determining the at least one quantity comprises: determining, based on the operational data, a temporal progression of a rotational speed of a rotary drive driving a displacement pump of a hydraulic system of the at least one piece of lifting equipment; and determining whether an anomaly occurs in the temporal progression of the rotational speed, wherein the at least one quantity indicates whether an anomaly occurs in the temporal progression of the rotational speed.

[0070] Another example (e.g., example 16) relates to a previous example (e.g., one of the examples 1 to 15) or to any other example, wherein determining the at least one quantity comprises: determining, based on the operational data, a respective pattern of power consumed by a rotary drive driving a displacement pump of a hydraulic system of the at least one piece of lifting equipment for at least one type of movement of the at least one piece of lifting equipment; and determining, based on the operational data, whether the respective pattern changes over time, wherein the at least one quantity indicates whether the respective pattern changes over time.

[0071] Another example (e.g., example 17) relates to a previous example (e.g., one of the examples 1 to 16) or to any other example, wherein the at least one piece of lifting equipment is a loader crane.

[0072] Another example (e.g., example 18) relates to a computing cloud comprising at least interface circuitry and processing circuitry configured to perform the method according to a previous example (e.g., one of the examples 1 to 17) or to any other example.

[0073] Another example (e.g., example 19) relates to a non-transitory machine-readable medium having stored thereon a program having a program code for performing the method according to a previous example (e.g., one of the examples 1 to 17) or to any other example, when the program is executed on a processor or a programmable hardware.

[0074] Another example (e.g., example 20) relates to a program having a program code for performing the method according to a previous example (e.g., one of the examples 1 to 17) or to any other example, when the program is executed on a processor or a programmable hardware.

[0075] The aspects and features described in relation to a particular one of the previous examples may also be combined with one or more of the further examples to replace an identical or similar feature of that further example or to additionally introduce the features into the further example.

[0076] Examples may further be or relate to a (computer) program including a program code to execute one or more of the above methods when the program is executed on a computer, processor or other programmable hardware component. Thus, steps, operations or processes of different ones of the methods described above may also be executed by programmed computers, processors or other programmable hardware components. Examples may also cover program storage devices, such as digital data storage media, which are machine-, processor- or computer-readable and encode and / or contain machine-executable, processor-executable or computer-executable programs and instructions. Program storage devices may include or be digital storage devices, magnetic storage media such as magnetic disks and magnetic tapes, hard disk drives, or optically readable digital data storage media, for example. Other examples may also include computers, processors, control units, (field) programmable logic arrays ((F)PLAs), (field) programmable gate arrays ((F)PGAs), graphics processor units (GPU), application-specific integrated circuits (ASICs), integrated circuits (ICs) or system-on-a-chip (SoCs) systems programmed to execute the steps of the methods described above.

[0077] It is further understood that the disclosure of several steps, processes, operations or functions disclosed in the description or claims shall not be construed to imply that these operations are necessarily dependent on the order described, unless explicitly stated in the individual case or necessary for technical reasons. Therefore, the previous description does not limit the execution of several steps or functions to a certain order. Furthermore, in further examples, a single step, function, process or operation may include and / or be broken up into several sub-steps, -functions, -processes or -operations.

[0078] If some aspects have been described in relation to a device or system, these aspects should also be understood as a description of the corresponding method. For example, a block, device or functional aspect of the device or system may correspond to a feature, such as a method step, of the corresponding method. Accordingly, aspects described in relation to a method shall also be understood as a description of a corresponding block, a corresponding element, a property or a functional feature of a corresponding device or a corresponding system.

[0079] The following claims are hereby incorporated in the detailed description, wherein each claim may stand on its own as a separate example. It should also be noted that although in the claims a dependent claim refers to a particular combination with one or more other claims, other examples may also include a combination of the dependent claim with the subject matter of any other dependent or independent claim. Such combinations are hereby explicitly proposed, unless it is stated in the individual case that a particular combination is not intended. Furthermore, features of a claim should also be included for any other independent claim, even if that claim is not directly defined as dependent on that other independent claim.

Examples

Embodiment Construction

[0011]Some examples are now described in more detail with reference to the enclosed figures. However, other possible examples are not limited to the features of these embodiments described in detail. Other examples may include modifications of the features as well as equivalents and alternatives to the features. Furthermore, the terminology used herein to describe certain examples should not be restrictive of further possible examples.

[0012]Throughout the description of the figures same or similar reference numerals refer to same or similar elements and / or features, which may be identical or implemented in a modified form while providing the same or a similar function. The thickness of lines, layers and / or areas in the figures may also be exaggerated for clarification.

[0013]When two elements A and B are combined using an "or", this is to be understood as disclosing all possible combinations, i.e. only A, only B as well as A and B, unless expressly defined otherwise in the individual...

Field

[0001] The present disclosure relates to monitoring of lifting equipment. In particular, examples of the present disclosure relate to a computing cloud and a method for a cloud source for monitoring at least one piece of lifting equipment.Background

[0002] Cranes, in particular loader cranes play a pivotal role in various industries, providing efficient lifting and handling capabilities. However, operators or owners of cranes have no or only very limited technical insights on the operation of the cranes. This unnecessarily complicates a large number of tasks, such as crane maintenance. The same problem arises with other types of lifting equipment equipped with a hydraulic system, such as forklifts or lifting platforms.

[0003] Hence, there may be a demand for improved monitoring of lifting equipment.Summary

[0004] This demand is met by a method for a cloud source for monitoring at least one piece of lifting equipment, a computing cloud, a non-transitory machine-readable medium and a program in accordance with the independent claims. Advantageous embodiments are defined by the dependent claims.

[0005] According to a first aspect, the present disclosure provides a method for a cloud source for monitoring at least one piece of lifting equipment. The method comprises receiving operational data of the at least one piece of lifting equipment. Additionally, the method comprises determining at least one quantity characterizing the at least one piece of lifting equipment based on the operational data.

[0006] According to a second aspect, the present disclosure provides a computing cloud comprising at least interface circuitry and processing circuitry configured to perform the method according to the first aspect.

[0007] According to a third aspect, the present disclosure provides a non-transitory machine-readable medium having stored thereon a program having a program code for performing the method according to the first aspect, when the program is executed on a processor or a programmable hardware.

[0008] According to a fourth aspect, the present disclosure provides a program having a program code for performing the method according to the first aspect, when the program is executed on a processor or a programmable hardware.

[0009] The proposed technology allows for remote monitoring and management of lifting equipment. This enables centralized oversight from any location with internet access. Furthermore, real-time data processing and analysis is enabled. Accordingly, operational data from lifting equipment can be analyzed instantly, leading to timely decision-making and quicker response to potential issues. Furthermore, various applications such as predictive maintenance are enabled, thus reducing downtime and improving equipment lifespan. Monitoring operational data and determining key quantities may further enhance safety. It is possible to identify potentially problematic operating conditions, detect anomalies, and provide alerts or automatic shutdowns to prevent accidents. Furthermore, optimization of equipment usage may be enabled by analyzing usage patterns and operational efficiency. This can lead to better scheduling, reduced energy consumption, and overall improved performance of the lifting equipment. Accordingly, the sustainability of the lifting equipment may be improved. The cloud-based technology is inherently scalable allowing to accommodate more data and more complex analysis without significant changes to the infrastructure as the amount of lifting equipment grows. Overall, the proposed technology leverages cloud technology to provide a comprehensive, efficient, and scalable solution for monitoring and managing lifting equipment, enhancing various aspects such as operational efficiency, safety, and maintenance practices.Brief description of the Figures

[0010] Some examples of apparatuses and / or methods will be described in the following by way of example only, and with reference to the accompanying figures, in which Fig. 1 illustrates a flowchart of an example of a method for a cloud source for monitoring at least one piece of lifting equipment; and Fig. 2 illustrates an exemplary computing cloud. Detailed Description

[0011] Some examples are now described in more detail with reference to the enclosed figures. However, other possible examples are not limited to the features of these embodiments described in detail. Other examples may include modifications of the features as well as equivalents and alternatives to the features. Furthermore, the terminology used herein to describe certain examples should not be restrictive of further possible examples.

[0012] Throughout the description of the figures same or similar reference numerals refer to same or similar elements and / or features, which may be identical or implemented in a modified form while providing the same or a similar function. The thickness of lines, layers and / or areas in the figures may also be exaggerated for clarification.

[0013] When two elements A and B are combined using an "or", this is to be understood as disclosing all possible combinations, i.e. only A, only B as well as A and B, unless expressly defined otherwise in the individual case. As an alternative wording for the same combinations, "at least one of A and B" or "A and / or B" may be used. This applies equivalently to combinations of more than two elements.

[0014] If a singular form, such as "a", "an" and "the" is used and the use of only a single element is not defined as mandatory either explicitly or implicitly, further examples may also use several elements to implement the same function. If a function is described below as implemented using multiple elements, further examples may implement the same function using a single element or a single processing entity. It is further understood that the terms "include", "including", "comprise" and / or "comprising", when used, describe the presence of the specified features, integers, steps, operations, processes, elements, components and / or a group thereof, but do not exclude the presence or addition of one or more other features, integers, steps, operations, processes, elements, components and / or a group thereof.

[0015] Fig. 1 illustrates a method 100 for (performed by) a cloud source for monitoring at least one piece of lifting equipment.

[0016] A cloud service, as used in the present disclosure, is a remotely accessible computing resource or set of resources delivered over a network, such as the internet. These resources may, e.g., encompass computing power, storage, databases, software applications and networking capabilities. For example, a computing cloud or a data center may host (run) the cloud service.

[0017] A piece of lifting equipment is any machinery (device) for lifting loads (e.g., goods and / or people) using hydraulic power. For example, the lifting equipment may be a crane such as a knuckle boom or loader crane for loading and unloading goods (loads). Alternatively, the lifting equipment may, e.g., be the crane section of a mobile crane (with a vehicle being a mobile platform having mounted thereon the crane section) or the entire mobile crane. In other examples, the lifting equipment may, e.g., be a forklift comprising a pronged device in the front, called forks, which can be inserted under loads to lift and carry them. In still other examples, the lifting equipment may, e.g., be a lifting platform (elevating platform of lift table) comprising a platform supported by a mechanical structure in a liftable manner to a base of the lifting platform.

[0018] The method 100 comprises receiving 102 operational data of the at least one piece of lifting equipment. The operational data refers to various types of information that describe the performance, status, and conditions under which the respective lifting equipment operates. For example, the operational data may indicate the weight of load being lifted, the duration of operation or usage patterns, the current (operational) status such as a current operation mode (e.g., active, idle, power mode, eco-mode, etc.), performance metrics such as lift speed or energy consumption. However, it is to be noted that the present disclosure is not limited to the foregoing examples. The operational data may indicate more, less or different pieces of information. The operational data may relate to (originate from) a single piece of lifting equipment like a single crane or to (from) multiple (i.e., two or more) pieces of lifting equipment like two or more cranes. The operational data may be received directly from the respective lifting equipment or alternatively from an intermediate device such as a remote server (e.g., of an operator or owner of the respective lifting equipment) communicatively coupled between the respective lifting equipment and the cloud service. The operational data may, e.g., be received continuously and / or aggregated.

[0019] Additionally, the method 100 comprises determining 104 at least one quantity characterizing the at least one piece of lifting equipment based on the operational data. In other words, the operational data are analyzed to determine (e.g., calculate) one or more specific quantities that describe or characterize the equipment's performance, condition, or usage. The one or more determined quantities provide insight into specific attributes or performance metrics of the lifting equipment. These quantities may help in understanding various aspects of the respective lifting equipment such as its functioning, efficiency, etc. Various exemplary quantities will be described below. However, it is to be noted that the present disclosure is not limited to these specific examples. The at least one quantity may be determined based on at least one of real-time operational data (i.e., operational data that is delivered / received immediately after collection / generation without significant delay) and historical operational data (i.e., operational data collected over a period of time in the past). For example, a computational model may be used for determining the at least one quantity. The computational model is a data analysis model for deriving the at least one quantity from the input operational data. The computational model may, e.g., be a mechanistic (i.e., rule-based) model, a trained machine-learning model or a combination thereof. The computational model may use various signal and / or data processing operations such as signal / data addition, subtraction, multiplication, division, integration, derivation, filtering (e.g., discrete, continuous or both), delaying, etc. to determine the at least one quantity from the input operational data.

[0020] The method 100 allows for remote monitoring and management of lifting equipment. This enables centralized oversight from any location with internet access. Furthermore, real-time data processing and analysis is enabled. Accordingly, operational data from lifting equipment can be analyzed instantly, leading to timely decision-making and quicker response to potential issues. Furthermore, various applications such as predictive maintenance are enabled, thus reducing downtime and improving equipment lifespan. Monitoring operational data and determining key quantities may further enhance safety. It is possible to identify potentially problematic operating conditions, detect anomalies, and provide alerts or automatic shutdowns to prevent accidents. Furthermore, optimization of equipment usage may be enabled by analyzing usage patterns and operational efficiency. This can lead to better scheduling, reduced energy consumption, and overall improved performance of the lifting equipment. Accordingly, the sustainability of the lifting equipment may be improved. The cloud-based technology is inherently scalable allowing to accommodate more data and more complex analysis without significant changes to the infrastructure as the amount of lifting equipment grows. Overall, the method 100 leverages cloud technology to provide a comprehensive, efficient, and scalable solution for monitoring and managing lifting equipment, enhancing various aspects such as operational efficiency, safety, and maintenance practices.

[0021] According to examples of the present disclosure, the method 100 may further comprise providing (making available) 106 data about the at least one quantity for retrieval by an application executed on a terminal device of a user. For example, the data about the at least one quantity may be stored in a cloud storage or another storage system accessible by the application. The provisioning of the data about the at least one quantity for retrieval by the application enables users to easily access and interact with the data from their terminal devices, enhancing usability and convenience. The terminal device of the user may, e.g., be a mobile terminal device such as a mobile phone, a tablet-computer, or a laptop computer, or a stationary terminal device such as a computer. The application may, e.g., be an application specifically provided for monitoring the at least one piece of lifting equipment. Alternatively, the application can also be a universal application (e.g., an Internet browser). The information about the at least one quantity characterizing the at least one piece of lifting equipment may, e.g., be provided as a value or series of values retrievable via the application or as a graphic retrievable via the application. Accordingly, the information about the at least one quantity characterizing the at least one piece of lifting equipment may be provided in a format that is easy to understand for the user. For example, the application may offer features for visualizing and analyzing the data, helping users to understand trends, detect anomalies, and make informed decisions.

[0022] In some examples of the present disclosure, the data about the at least one quantity characterizing the at least one piece of lifting equipment may be provided on a website with access restricted to a predetermined user group. The provision of the information about the at least one quantity characterizing the at least one piece of lifting equipment on a web site enables access at any time and place to the at least one quantity characterizing the at least one piece of lifting equipment by the user, and thus a flexible monitoring of the at least one piece of lifting equipment. The output of the information via a web site may further enable the user to create individual notifications using appropriate configuration options on the web site. In addition, the access restriction enables the monitoring to be secured against access by unauthorized third parties. Access can be restricted, for example, by a password, a security certificate, or local access restrictions.

[0023] The data about the at least one quantity characterizing the at least one piece of lifting equipment may optionally be stored in a cloud storage or another storage system. Consequently, the information about the at least one quantity characterizing the at least one piece of lifting equipment may be accessed flexibly in terms of time and location, with a minimized risk of data loss.

[0024] According to examples of the present disclosure, determining 104 the at least one quantity characterizing the at least one piece of lifting equipment may be carried out continuously. In other words, the process of determining the at least one quantity characterizing the at least one piece of lifting equipment may be performed on an ongoing, uninterrupted basis. This means that the incoming operational data is constantly analyzed to update the one or more quantities in, e.g., real-time or near real-time. Continuous determination allows for real-time monitoring of the at least one piece of lifting equipment. A user may be permanently provided with current values for the at least one characterizing quantity for retrieval. In addition, the continuous determination of the at least one quantity characterizing quantity enables determining temporal courses or temporal changes of the at least one characterizing quantity, which in turn may themselves characterize the at least one piece of lifting equipment or the operating behavior thereof.

[0025] In alternative examples of the present disclosure, the at least one quantity characterizing the at least one piece of lifting equipment may be determined discontinuously - for example, periodically or as a result of the occurrence or detection of the occurrence of a predetermined event. If, for example, a specific quantity characterizing the at least one piece of lifting equipment is required only once a year to prepare a legally required report, it may be sufficient to determine this quantity only once a year from the system data. In this way, calculation effort can be saved for unnecessary determinations (e.g. calculations) of the at least one quantity characterizing the at least one piece of lifting equipment.

[0026] In some examples of the present disclosure, the at least one quantity may be determined for operational data for (of) at least two different operating periods of the at least one lifting equipment. In other words, the method 100 may comprise determining 104 the at least one quantity separately for at least two distinct time periods during which the at least one piece of lifting equipment is operating. These periods may, e.g., be hours of the day, days, weeks, months, or any defined intervals of operation. In these examples, the method 100 further comprises comparing 108 the determined at least one quantities for the at least two different operating periods. In other words, after determining the at least one quantity for the different operating periods, the method 100 comprises comparing these quantities to, e.g., analyze changes, trends, or patterns over time. The comparison may enable identification of trends and patterns in the lifting equipment's performance and usage, which can be critical for long-term planning and decision-making. Furthermore, the comparison may allow for benchmarking performance across different periods, helping to assess the impact of changes in operations, maintenance practices, or environmental conditions. The comparison may further provide insights into how the condition of the lifting equipment evolves over time, supporting maintenance planning and lifecycle management.

[0027] For example, an owner or operator of a piece of lifting equipment such as a crane may use the method to continuously collect the operational data over a span of several months. One or more quantities characterizing the piece of lifting equipment is determined for each month. The determined quantities are then compared month-over-month. The comparison may reveal various aspects. For example, the comparison may reveal that the piece of lifting equipment has a declining efficiency trend, prompting a closer inspection and early maintenance intervention. In other examples, the comparison may reveal peak usage periods which may help in resource planning and allocation.

[0028] In some examples of the present disclosure, the at least one quantity may be determined for operational data of at least two pieces of lifting equipment. In other words, the method 100 may comprise determining 104 the at least one quantity separately for at least two different pieces of lifting equipment (e.g., two or more cranes). In these examples, the method 100 further comprises comparing 110 the determined at least one quantities for the at least two pieces of lifting equipment. In other words, after determining the at least one quantity for the different pieces of equipment, the method 100 comprises comparing 110 these quantities to, e.g., analyze performance, efficiency, or condition across multiple lifting equipment units. The comparison may allow for benchmarking performance across different lifting equipment units, e.g., helping to identify which equipment is performing better or worse. Furthermore, the identification of operational inefficiencies may be facilitated by comparing metrics across multiple lifting equipment units, leading to improved overall efficiency.

[0029] For example, an owner or operator of a piece of lifting equipment such as a crane may use the method to simultaneously collect the operational data of two pieces of lifting equipment used for the same purpose (e.g., loading goods onto a truck at a site A and unloading the goods from the truck at a site B). One or more quantities characterizing the respective lifting equipment are determined for each lifting equipment and compared to each other. The comparison may reveal various aspects. For example, the comparison may reveal that the pieces of lifting equipment show different performances, helping to identify underperforming units and potential areas for improvement. In other examples, the comparison may reveal varying maintenance needs of different equipment, supporting more targeted and effective maintenance strategies.

[0030] The determined at least one quantity may be used for predictive maintenance according to examples of the present disclosure. Accordingly, the method 100 may optionally further comprise predicting 112 failure of a component of the at least one piece of lifting equipment based on the determined at least one quantity. For example, a failure model may be used for predicting failure of the component of the at least one piece of lifting equipment. The failure model is a data analysis model for predicting failure of the component of the at least one piece of lifting equipment from the input at least one quantity and optionally further data. The failure model may, e.g., be a mechanistic (i.e., rule-based) model, a trained machine-learning model or a combination thereof. The failure model is configured to predict when a component of a lifting equipment is likely to fail, based on the analysis of the determined one or more quantities. This prediction may include estimating the time frame or operational cycles remaining before failure. Predictive maintenance enables maintenance to be performed proactively based on predicted failures, reducing the risk of unexpected breakdowns and extending the lifespan of the lifting equipment. By predicting failures before they occur, the method helps minimize unplanned downtime, keeping the lifting equipment operational and productive. Preventive maintenance based on accurate predictions can lead to significant cost savings by avoiding expensive emergency repairs and extending component life. This may further improve the sustainability of the lifting equipment. Predicting potential failures may enhance safety as potential issues may be addressed before they become serious.

[0031] Optionally, the method 100 may further comprise performing a predefined action based on the predicted failure of the component of the at least one piece of lifting equipment. The predefined action may be manifold. For example, the predefined action may be one or more of causing output of a notification (e.g., a message or an e-mail) about the failure on a terminal device of a user (e.g., a mobile phone or a tablet-computer of a user) and causing output of an alarm (e.g., activating of an audio alarm and / or a visual alarm). Accordingly, one or more user or people may be informed about the failure. Alternatively or additionally, the predefined action may further be one or more of causing the at least one piece of lifting equipment to change its operation (e.g., to immediately shut down). Accordingly, a potentially serious situation caused by the predicted failure may be avoided and, hence, safety be enhanced.

[0032] For example, an owner or operator of a piece of lifting equipment such as a crane may use the method to collect the operational data of the lifting equipment. Using the operational data, the system may, e.g., predict that a component such as a bearing, a valve or a hydraulic cylinder is likely to fail within the next 100 operational hours. Accordingly, a maintenance team may be alerted, who schedule a preventive replacement of the component (e.g., outside planned operating hours of the lifting equipment), thus avoiding failure of the component that could disrupt operations and incur significant costs.

[0033] As described above, the at least one quantity may be manifold. In the following, some examples will be described in greater. However, it is to be noted that the present disclosure is not limited to these specific examples.

[0034] In some examples of the present disclosure, the at least one quantity is a consumed energy per operation of the at least one piece of lifting equipment. In other words, the amount of energy consumed by the lifting equipment for each operation may be determined according to the method 100. The operation could refer to a single lift, a cycle of lifting and lowering, or any defined unit of activity performed by the at least one piece of lifting equipment.

[0035] For example, the operational data may indicate a start time t s and an end time t e for an operation as well as the power consumption P(t) of the lifting equipment over time. Accordingly, the consumed energy for an operation may be determined as follows: E = ∫ t s t e P t dt

[0036] In alternative example, the power consumption may be derived from the operational data. Various functions of lifting equipment such as lifting, lowering, extending, retracting, rotating, and stabilizing use hydraulic power provided by a displacement pump of a hydraulic system of a lifting equipment. Accordingly, the power consumption may be mainly determined by the power used for operating the hydraulic system. The displacement pump of the lifting equipment is driven by a rotary drive (e.g., a Power Take-Off, PTO, or an electric rotary drive, also known as electric Power Take-Off, ePTO) to provide hydraulic power. For example, the power consumption may be derived from the rotational speed of the rotary drive driving the displacement pump of the lifting equipment's hydraulic system. The operational data may indicate the rotational speed. Similarly, the operational data may indicate a torque provided by the rotary drive and the power consumption may be derived from the torque. The start time t s and the end time t e for an operation may, e.g., be defined by the activation (engagement) period or time frame of the rotary drive.

[0037] Monitoring the consumed energy per operation may enables monitoring of the energy efficiency of the lifting equipment, helping to identify inefficient operations and opportunities for energy savings. By understanding energy consumption patterns, operators or owners may manage and reduce energy costs, leading to significant savings over time. Analyzing energy consumption per operation may help in optimizing the performance of the lifting equipment by identifying and addressing factors that contribute to excessive energy use. Reducing energy consumption also reduces the environmental impact of the lifting equipment, promoting sustainability. Changes in energy consumption patterns can be an indicator of potential issues or wear in the lifting equipment, supporting predictive maintenance strategies. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the consumed energy per operation.

[0038] Analogously to the consumed energy per operation, the at least one quantity may be a consumed energy per operation day of the at least one piece of lifting equipment. In other words, the amount of energy consumed by the lifting equipment over the course of an entire operation day may be determined according to the method 100. The operation day may refer to a 24-hour period or the specific working hours in which the lifting equipment is used during a day. For example, the consumed amounts of energy for all operations during the operation day may be summed up. This enables monitoring of the energy efficiency of the lifting equipment or other related parameters on a daily basis rather than on a per operation basis.

[0039] Analogously to the consumed energy per operation and the consumed energy per operation day, the at least one quantity may be a consumed energy over the (entire) lifetime of the at least one piece of lifting equipment. In other words, the total amount of energy consumed by the lifting equipment over its entire operational lifetime may be determined according to the method 100. The lifetime refers to the period from the initial use of the lifting equipment to the present (or until the lifting equipment is decommissioned or replaced). For example, the consumed amounts of energy for all operations during the lifetime may be summed up. This enables monitoring of the energy efficiency of the lifting equipment or other related parameters over its entire operational lifetime, helping to identify long-term inefficiencies and opportunities for improvement.

[0040] In some examples, the at least one quantity may concern the hydraulic system of the lifting equipment. For example, the at least one quantity may be a number of pump circulations of the displacement pump of the hydraulic system of the at least one piece of lifting equipment. In other words, the count of pump circulations or cycles of the displacement pump within the hydraulic system of the lifting equipment may be determined. A circulation or cycle refers to one complete operational cycle of the displacement pump, from intake to discharge. The number of pump circulations may be determined per operation, per operation day or over the (entire) lifetime of the at least one piece of lifting equipment. For example, the number of pump circulations may be derived from the operating periods of the rotary drive driving the displacement pump and the rotational speed of the rotary drive. The operational data may indicate the rotational speed. The operating periods of the rotary drive may, e.g., be defined by the activation (engagement) period or time frame of the rotary drive. Accordingly, placement of a dedicated sensor for measuring the number of pump circulations may avoided.

[0041] However, in alternative examples, the pump circulations measured by a dedicated senor may be integrated for the operating periods of the rotary drive to determine the number of pump circulations. The number of pump circulations may enable detailed monitoring of the hydraulic system's performance by tracking the activity of the displacement pump, a critical component in the system. By monitoring the number of pump circulations, the method 100 may help predict when the displacement pump or other hydraulic components may require maintenance or replacement, reducing the risk of unexpected failures. The number of pump circulations may provide insights into the wear and tear on the displacement pump, supporting better management of the component's lifespan and planning for timely maintenance. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the number of pump circulations. Analyzing the pump circulation data may further reveal inefficiencies or overuse in the hydraulic system, guiding optimization efforts to improve overall operational efficiency.

[0042] In some examples, the at least one quantity is a predefined number of maximum pump speeds of the displacement pump of the hydraulic system of the at least one piece of lifting equipment. In other words, a predefined number of the highest pump speeds achieved (driven) by the displacement pump during operation may be determined according to the method 100 (e.g., the five, ten, 15 or 20 highest pump speeds). Only pump speeds that were achieved (driven) by the displacement pump for at least a predefined time period (e.g., 1 second, 2 seconds, 5 seconds, 10 seconds, 30 seconds or more) may be determined. For example, the pump speeds may be derived from the rotational speed of the rotary drive. However, in alternative examples, the pump speeds may be measured by a dedicated senor.

[0043] The maximum pump speeds achieved by the displacement pump may enable detailed monitoring of the displacement pump's performance by tracking instances of maximum speed operation, which can indicate high-demand periods or potential stress on the system. Analyzing the data on maximum pump speeds can reveal inefficiencies or overuse in the hydraulic system, guiding optimization efforts to improve overall operational efficiency. Furthermore, the maximum pump speeds provide insights into the wear and tear on the displacement pump, supporting better management of the component's lifespan and planning for timely maintenance. By monitoring the maximum pump speeds, the method 100 may help predict when the pump or other hydraulic components may require maintenance or replacement, reducing the risk of unexpected failures. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the predefined number of maximum pump speeds of the displacement pump.

[0044] In some examples, the at least one quantity may be a predefined number of maximum temperatures measured in the hydraulic system of the at least one piece of lifting equipment. In other words, a predefined number of the highest temperatures measured in the hydraulic system may be determined according to the method 100 (e.g., the five, ten, 15 or 20 highest temperatures). Only temperatures that were measured in the hydraulic system for at least a predefined time period (e.g., 1 second, 2 seconds, 5 seconds, 10 seconds, 30 seconds or more) may be determined. For example, the temperatures may be measured by a dedicated senor. Temperature is an important parameter as it can indicate the health and performance of the hydraulic system. The measured temperatures provides insights into the wear and tear on the hydraulic system, supporting better management of the components' lifespan and planning for timely maintenance. By monitoring the maximum temperatures, the method 100 may help predict when the hydraulic system or its components may require maintenance or replacement, reducing the risk of unexpected failures. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the predefined number of maximum temperatures measured in the hydraulic system

[0045] According to some examples, the at least one quantity is a moved volume of hydraulic fluid in the hydraulic system of the at least one piece of lifting equipment. In other words, the volume of hydraulic fluid moved within the hydraulic system may be determined according to the method 100. The moved volume of hydraulic fluid may be determined per operation, per operation day or over the (entire) lifetime of the at least one piece of lifting equipment. In general, any type of hydraulic fluid suitable for transmitting power efficiently may be used in the hydraulic system. For example, the hydraulic fluid may be a mineral oil-based hydraulic fluid or a synthetic hydraulic fluid. The volume of hydraulic fluid moved is an indicator of the hydraulic system's activity and performance. For example, the volume of hydraulic fluid moved within the hydraulic system may be derived from the operating periods of the rotary drive driving the displacement pump and the rotational speed of the rotary drive. The operational data may indicate the rotational speed. The operating periods of the rotary drive may, e.g., be defined by the activation (engagement) period or time frame of the rotary drive. Further parameters such as a load sensing signal of the displacement pump or the swivel angle of the displacement pumps swash plate may be indicated by the operational data and be taken into account for the determination of the volume of hydraulic fluid moved within the hydraulic system. Accordingly, placement of a dedicated sensor for measuring the volume of hydraulic fluid moved within the hydraulic system may avoided. However, in alternative examples, the volume of hydraulic fluid moved within the hydraulic system measured by a dedicated senor may be integrated for the operating periods of the rotary drive to determine the total volume of hydraulic fluid moved within the hydraulic system. Tracking the volume of hydraulic fluid moved may enables detailed monitoring of the hydraulic system's performance, which is crucial for understanding hydraulic system activity and efficiency. The moved volume of hydraulic fluid in the hydraulic system may provide insights into the wear and tear on the hydraulic system, supporting better management of the components' lifespan and planning for timely maintenance. By monitoring the volume of hydraulic fluid moved, the method 100 may help predict when components of the hydraulic system may require maintenance or replacement (e.g., replacement of the hydraulic fluid or filters), reducing the risk of unexpected failures. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the moved volume of hydraulic fluid in the hydraulic system.

[0046] In some examples, determining 104 the at least one quantity may comprise determining, based on the operational data, a temporal progression of the rotational speed of the rotary drive driving the displacement pump of the hydraulic system of the at least one piece of lifting equipment. In other words, it is determined how the rotational speed of the rotary drive changes over time. For example, the operational data may indicate the torque and the rotational speed of the rotary device and these pieces of information may be analyzed to determine the temporal progression of the rotary drive's rotational speed. In these examples, determining 104 the at least one quantity may further comprise determining whether an anomaly occurs in the temporal progression of the rotational speed. The anomaly is an unusual or unexpected pattern in the temporal progression of the rotational speed. The anomaly may be anything that deviates from normal or expected patterns. In other words, the temporal progression of the rotary drive's rotational speed is analyzed to identify deviations from normal or expected behavior. Anomalies may, e.g., include sudden spikes or drops in speed, unusual fluctuations, or patterns that do not match historical data (e.g., oscillations). Anomalies may indicate potential issues such as mechanical problems, inefficiencies, or impending failures. The at least one quantity indicates whether an anomaly occurs in the temporal progression of the rotational speed. In other words, the at least one quantity is an indication of the presence or absence of anomalies. For example, an anomaly detection model may be used for determining whether an anomaly occurs in the temporal progression of the rotational speed. The anomaly detection model is a data analysis model for determining whether an anomaly occurs in the temporal progression of the rotational speed from the input temporal progression of the rotary drive's rotational speed and optionally further data. The anomaly detection model may, e.g., be a mechanistic (i.e., rule-based) model, a trained machine-learning model or a combination thereof. The anomaly detection model may configured to identify anomalies based on predefined criteria, historical data or trained knowledge.

[0047] By monitoring and analyzing the temporal progression of the rotational speed, the method 100 may detect early signs of mechanical issues or inefficiencies, allowing for timely maintenance and preventing unexpected failures. For example, swinging and vibrations within the hydraulic system may be detected, helping in the predictive maintenance of the hydraulic system. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the detected occurrence of an anomaly.

[0048] According to some examples, determining 104 the at least one quantity may comprise determining, based on the operational data, a respective pattern of power consumed by the rotary drive driving the displacement pump of the hydraulic system of the at least one piece of lifting equipment for at least one type of movement of the at least one piece of lifting equipment. In other words, the power consumption profile(s) for one or more specific types of movements performed by the lifting equipment (e.g., lifting, lowering, rotating, extending) are determined. For example, the operational data may indicate the torque and the rotational speed of the rotary device and these pieces of information may be analyzed to determine the patterns of consumed power. In these examples, determining 104 the at least one quantity may further comprise determining, based on the operational data, whether the respective pattern changes over time. In other words, it is determined how the power consumption pattern evolves or deviates over time. For example, the power consumption patterns may be continuously monitored and compared to established profiles. Changes in the pattern are detected by identifying deviations from the expected power consumption. For example, it may be detected that rotational speed of the rotary drives decreased for the same consumed power and load that is being lifted. The at least one quantity indicates whether the respective pattern changes over time. In other words, the at least one quantity is an indication of the presence or absence of changes in power consumption patterns.

[0049] By monitoring changes in power consumption patterns, the method 100 may detect early signs of mechanical issues or inefficiencies, allowing for timely maintenance and preventing unexpected failures. For example, increasing friction in the hydraulic system may cause an increased power consumption for the same operation over time. Detecting changes in the power consumption may help in the predictive maintenance of the hydraulic system. Accordingly, predicting 112 failure of a component of the at least one piece of lifting equipment may be based on the detection of one or more patterns changing over time.

[0050] Fig. 2 further illustrates an exemplary computing cloud or data center 200 for performing the method 100 described above.

[0051] The computing cloud 200 comprises at least interface circuitry 210 and processing circuitry 220. The interface circuitry 210 and the processing circuitry 220 are configured to perform the method 100 described above.

[0052] The interface circuitry 210 is configured for interfacing and data exchange with external entities. For example, the interface circuitry 210 may be configured to receive the operational data of the at least one piece of lifting equipment directly from the respective lifting equipment or alternatively from an intermediate device such as a remote server of an operator or owner of the respective lifting equipment. The interface circuitry 210 may be configured for wireless and / or wired communication with the external entities.

[0053] The processing circuitry 220 is communicatively coupled to the interface circuitry 210. For example, the processing circuitry 210 may be a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which or all of which may be shared, a digital signal processor (DSP) hardware, an application specific integrated circuit (ASIC), a system-on-a-chip (SOC), a neuromorphic processor or a field programmable gate array (FPGA). The computing cloud 200 may comprise memory configured to store instructions, which when executed by the processing circuitry 220, cause the processing circuitry 220 and the interface circuitry 210 to perform the method 100.

[0054] Analogously to what is described above for the method 100, the computing cloud 200 may allow for remote monitoring and management of lifting equipment. The computing cloud 200 may allow to provide a comprehensive, efficient, and scalable solution for monitoring and managing lifting equipment, enhancing various aspects such as operational efficiency, safety, sustainability and maintenance practices.

[0055] The examples described herein may be summarized as follows: An example (e.g., example 1) relates to a method for a cloud source for monitoring at least one piece of lifting equipment. The method comprises receiving operational data of the at least one piece of lifting equipment. Additionally, the method comprises determining at least one quantity characterizing the at least one piece of lifting equipment based on the operational data.

[0056] Another example (e.g., example 2) relates to a previous example (e.g., example 1) or to any other example, further comprising providing data about the at least one quantity for retrieval by an application executed on a terminal device of a user.

[0057] Another example (e.g., example 3) relates to a previous example (e.g., example 2) or to any other example, wherein the data about the at least one quantity are provided on a website with access restricted to a predetermined user group.

[0058] Another example (e.g., example 4) relates to a previous example (e.g., one of the examples 1 to 3) or to any other example, wherein determining the at least one quantity is carried out continuously.

[0059] Another example (e.g., example 5) relates to a previous example (e.g., one of the examples 1 to 4) or to any other example, wherein the at least one quantity is determined for operational data for at least two different operating periods of the at least one lifting equipment, and wherein the method further comprises comparing the determined at least one quantities for the at least two different operating periods.

[0060] Another example (e.g., example 6) relates to a previous example (e.g., one of the examples 1 to 5) or to any other example, wherein the at least one quantity is determined for operational data of at least two pieces of lifting equipment, and wherein the method further comprises comparing the determined at least one quantities for the at least two pieces of lifting equipment.

[0061] Another example (e.g., example 7) relates to a previous example (e.g., one of the examples 1 to 6) or to any other example, further comprising predicting failure of a component of the at least one piece of lifting equipment based on the at least one quantity.

[0062] Another example (e.g., example 8) relates to a previous example (e.g., one of the examples 1 to 7) or to any other example, wherein the at least one quantity is a consumed energy per operation of the at least one piece of lifting equipment.

[0063] Another example (e.g., example 9) relates to a previous example (e.g., one of the examples 1 to 8) or to any other example, wherein the at least one quantity is a consumed energy per operation day of the at least one piece of lifting equipment.

[0064] Another example (e.g., example 10) relates to a previous example (e.g., one of the examples 1 to 9) or to any other example, wherein the at least one quantity is a consumed energy over the lifetime of the at least one piece of lifting equipment.

[0065] Another example (e.g., example 11) relates to a previous example (e.g., one of the examples 1 to 10) or to any other example, wherein the at least one quantity is a number of pump circulations of a displacement pump of a hydraulic system of the at least one piece of lifting equipment.

[0066] Another example (e.g., example 12) relates to a previous example (e.g., one of the examples 1 to 11) or to any other example, wherein the at least one quantity is a predefined number of maximum pump speeds of a displacement pump of a hydraulic system of the at least one piece of lifting equipment.

[0067] Another example (e.g., example 13) relates to a previous example (e.g., one of the examples 1 to 12) or to any other example, wherein the at least one quantity is a predefined number of maximum temperatures measured in a hydraulic system of the at least one piece of lifting equipment.

[0068] Another example (e.g., example 14) relates to a previous example (e.g., one of the examples 1 to 13) or to any other example, wherein the at least one quantity is a moved volume of hydraulic fluid in a hydraulic system of the at least one piece of lifting equipment.

[0069] Another example (e.g., example 15) relates to a previous example (e.g., one of the examples 1 to 14) or to any other example, wherein determining the at least one quantity comprises: determining, based on the operational data, a temporal progression of a rotational speed of a rotary drive driving a displacement pump of a hydraulic system of the at least one piece of lifting equipment; and determining whether an anomaly occurs in the temporal progression of the rotational speed, wherein the at least one quantity indicates whether an anomaly occurs in the temporal progression of the rotational speed.

[0070] Another example (e.g., example 16) relates to a previous example (e.g., one of the examples 1 to 15) or to any other example, wherein determining the at least one quantity comprises: determining, based on the operational data, a respective pattern of power consumed by a rotary drive driving a displacement pump of a hydraulic system of the at least one piece of lifting equipment for at least one type of movement of the at least one piece of lifting equipment; and determining, based on the operational data, whether the respective pattern changes over time, wherein the at least one quantity indicates whether the respective pattern changes over time.

[0071] Another example (e.g., example 17) relates to a previous example (e.g., one of the examples 1 to 16) or to any other example, wherein the at least one piece of lifting equipment is a loader crane.

[0072] Another example (e.g., example 18) relates to a computing cloud comprising at least interface circuitry and processing circuitry configured to perform the method according to a previous example (e.g., one of the examples 1 to 17) or to any other example.

[0073] Another example (e.g., example 19) relates to a non-transitory machine-readable medium having stored thereon a program having a program code for performing the method according to a previous example (e.g., one of the examples 1 to 17) or to any other example, when the program is executed on a processor or a programmable hardware.

[0074] Another example (e.g., example 20) relates to a program having a program code for performing the method according to a previous example (e.g., one of the examples 1 to 17) or to any other example, when the program is executed on a processor or a programmable hardware.

[0075] The aspects and features described in relation to a particular one of the previous examples may also be combined with one or more of the further examples to replace an identical or similar feature of that further example or to additionally introduce the features into the further example.

[0076] Examples may further be or relate to a (computer) program including a program code to execute one or more of the above methods when the program is executed on a computer, processor or other programmable hardware component. Thus, steps, operations or processes of different ones of the methods described above may also be executed by programmed computers, processors or other programmable hardware components. Examples may also cover program storage devices, such as digital data storage media, which are machine-, processor- or computer-readable and encode and / or contain machine-executable, processor-executable or computer-executable programs and instructions. Program storage devices may include or be digital storage devices, magnetic storage media such as magnetic disks and magnetic tapes, hard disk drives, or optically readable digital data storage media, for example. Other examples may also include computers, processors, control units, (field) programmable logic arrays ((F)PLAs), (field) programmable gate arrays ((F)PGAs), graphics processor units (GPU), application-specific integrated circuits (ASICs), integrated circuits (ICs) or system-on-a-chip (SoCs) systems programmed to execute the steps of the methods described above.

[0077] It is further understood that the disclosure of several steps, processes, operations or functions disclosed in the description or claims shall not be construed to imply that these operations are necessarily dependent on the order described, unless explicitly stated in the individual case or necessary for technical reasons. Therefore, the previous description does not limit the execution of several steps or functions to a certain order. Furthermore, in further examples, a single step, function, process or operation may include and / or be broken up into several sub-steps, -functions, -processes or -operations.

[0078] If some aspects have been described in relation to a device or system, these aspects should also be understood as a description of the corresponding method. For example, a block, device or functional aspect of the device or system may correspond to a feature, such as a method step, of the corresponding method. Accordingly, aspects described in relation to a method shall also be understood as a description of a corresponding block, a corresponding element, a property or a functional feature of a corresponding device or a corresponding system.

[0079] The following claims are hereby incorporated in the detailed description, wherein each claim may stand on its own as a separate example. It should also be noted that although in the claims a dependent claim refers to a particular combination with one or more other claims, other examples may also include a combination of the dependent claim with the subject matter of any other dependent or independent claim. Such combinations are hereby explicitly proposed, unless it is stated in the individual case that a particular combination is not intended. Furthermore, features of a claim should also be included for any other independent claim, even if that claim is not directly defined as dependent on that other independent claim.

Examples

Embodiment Construction

[0011]Some examples are now described in more detail with reference to the enclosed figures. However, other possible examples are not limited to the features of these embodiments described in detail. Other examples may include modifications of the features as well as equivalents and alternatives to the features. Furthermore, the terminology used herein to describe certain examples should not be restrictive of further possible examples.

[0012]Throughout the description of the figures same or similar reference numerals refer to same or similar elements and / or features, which may be identical or implemented in a modified form while providing the same or a similar function. The thickness of lines, layers and / or areas in the figures may also be exaggerated for clarification.

[0013]When two elements A and B are combined using an "or", this is to be understood as disclosing all possible combinations, i.e. only A, only B as well as A and B, unless expressly defined otherwise in the individual...

Claims

1. A method (100) for a cloud source for monitoring at least one piece of lifting equipment, the method (100) comprising: receiving (102) operational data of the at least one piece of lifting equipment; and determining (104) at least one quantity characterizing the at least one piece of lifting equipment based on the operational data.

2. The method (100) of claim 1, further comprising: providing (106) data about the at least one quantity for retrieval by an application executed on a terminal device of a user.

3. The method (100) of claim 1 or claim 2, wherein the at least one quantity is determined for operational data for at least two different operating periods of the at least one lifting equipment, and wherein the method (100) further comprises comparing (108) the determined at least one quantities for the at least two different operating periods.

4. The method (100) of any one of claims 1 to 3, wherein the at least one quantity is determined for operational data of at least two pieces of lifting equipment, and wherein the method (100) further comprises comparing (110) the determined at least one quantities for the at least two pieces of lifting equipment.

5. The method (100) of any one of claims 1 to 4, further comprising: predicting (112) failure of a component of the at least one piece of lifting equipment based on the at least one quantity.

6. The method (100) of any one of claims 1 to 5, wherein the at least one quantity is a consumed energy per operation of the at least one piece of lifting equipment.

7. The method (100) of any one of claims 1 to 6, wherein the at least one quantity is a consumed energy per operation day of the at least one piece of lifting equipment.

8. The method (100) of any one of claims 1 to 7, wherein the at least one quantity is a consumed energy over the lifetime of the at least one piece of lifting equipment.

9. The method (100) of any one of claims 1 to 8, wherein the at least one quantity is a number of pump circulations of a displacement pump of a hydraulic system of the at least one piece of lifting equipment.

10. The method (100) of any one of claims 1 to 9, wherein the at least one quantity is a predefined number of maximum pump speeds of a displacement pump of a hydraulic system of the at least one piece of lifting equipment.

11. The method (100) of any one of claims 1 to 10, wherein the at least one quantity is a predefined number of maximum temperatures measured in a hydraulic system of the at least one piece of lifting equipment.

12. The method (100) of any one of claims 1 to 11, wherein the at least one quantity is a moved volume of hydraulic fluid in a hydraulic system of the at least one piece of lifting equipment.

13. The method (100) of any one of claims 1 to 12, wherein determining (104) the at least one quantity comprises: determining, based on the operational data, a temporal progression of a rotational speed of a rotary drive driving a displacement pump of a hydraulic system of the at least one piece of lifting equipment; and determining whether an anomaly occurs in the temporal progression of the rotational speed, wherein the at least one quantity indicates whether an anomaly occurs in the temporal progression of the rotational speed.

14. The method (100) of any one of claims 1 to 13, wherein determining (104) the at least one quantity comprises: determining, based on the operational data, a respective pattern of power consumed by a rotary drive driving a displacement pump of a hydraulic system of the at least one piece of lifting equipment for at least one type of movement of the at least one piece of lifting equipment; and determining, based on the operational data, whether the respective pattern changes over time, wherein the at least one quantity indicates whether the respective pattern changes over time.

15. The method (100) of any one of claims 1 to 14, wherein the at least one piece of lifting equipment is a loader crane.

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