Method of operating lifting appliance for accurate predictive maintenance

Abstract

The invention relates to a method for operating a lifting appliance spanning a lifting area, the lifting appliance comprising N > = 2 movable parts for transporting a load from a starting point to a destination, the N movable parts being configured for linear movement or for angular movement along any of three orthogonal axes X, Y and Z. The method comprises transporting the load from the starting point to the destination point by selecting, in a control device, speed parameters for displacements of the N movable parts: determining (S1, S2) a set of speed parameters for displacements of the N movable parts belonging to an operating area for which the N movable parts are located, the predictive maintenance function of the lifting appliance produces a result above a determined precision threshold; a speed parameter is selected (S3) in the group that minimizes a travel time of the load from the starting point to the destination point.

Description

Technical Field

[0001] The present invention relates to the field of lifting appliances, such as cranes, gantry cranes or overhead mobile cranes. The present disclosure particularly relates to a method for optimizing the operation of such lifting appliances to allow reliable predictive maintenance. Background Art

[0002] As Figure 1 shown, a lifting appliance 1 such as a bridge crane, a gantry crane or an overhead mobile crane generally includes a trolley 2 that can move along a horizontal axis Y on a single beam or a set of rails 3. This first movement along axis Y is generally referred to as a short-stroke movement and / or trolley movement. Depending on the type of appliance, the beam or set of rails 3 (also referred to as the bridge) can also move along a horizontal axis X perpendicular to axis Y, so that the trolley can move along axis X and axis Y. This second movement along axis X is generally referred to as a long-stroke movement and / or bridge, crane or gantry movement. The amounts of the effective short stroke along axis Y and the effective long stroke along axis X determine the lifting area spanned by the hoist 1.

[0003] A tool 4, also referred to as a load suspension device, is associated with a reeving system having a cable passing through the trolley 2, and the length of the cable 5 is controlled by the trolley 2 to be changed so that the load 6 can move along a vertical axis Z, referred to as a lifting movement.

[0004] In addition, some lifting appliances (such as cranes) can have additional movable parts that allow angular movement of the load, such as rotation about the vertical axis Z.

[0005] When used in an industrial environment or in ports and harbors, such lifting appliances operate with high dynamics and high speeds and are part of critical processes subject to high production requirements. Therefore, there is a great need for predictive maintenance of such lifting appliances, which allows detecting faults before they occur and notifying the operator of the lifting appliance accordingly. In fact, compared with planned interventions, repairing or replacing defective products always incurs higher costs:

[0006] - A fault may occur at a critical moment of the process (e.g., continuous casting) and result in significant production losses;

[0007] - Detecting faults, providing corresponding spare parts, preparing maintenance interventions, etc., also incur considerable financial and time losses.

[0008] So far, during the normal operation of lifting appliances, some suspicious phenomena can be directly detected by the operator, which provides an opportunity for the maintenance team to investigate impending failures. However, with the increasing use of automated systems, these early detections have disappeared, so a solution is needed that can determine the future failures of components with a high degree of confidence before they occur in the future.

[0009] To meet this need, a technical method used in the industry is to implement a digital twin-based solution that reproduces all or part of the normal behavior of the system. This solution relies on the development of one or several virtual sensors that can effectively predict physical values depending on the health of the machine, such as torque, vibration, or temperature.

[0010] The purpose of the virtual sensor is to predict the normal value at the precise operating point of the system and compare it with the actual measured value. A significant difference between the measured value and the predicted value is an indication of abnormal operation and implies that a failure may be impending.

[0011] The creation of virtual sensors usually implies a teaching phase to understand how the system operates. Relevant data directly affecting the physical values under consideration must be collected and recorded. For example, the temperature of an electric motor depends on the current, speed, and ambient temperature. If some of this data is missing, it is difficult or impossible to create relevant virtual sensors to accurately predict the temperature of the motor. Therefore, it is crucial to collect sufficient relevant data to inform the models that virtual sensors rely on during the learning phase.

[0012] However, one of the most common problems encountered during the learning phase is the difficulty (or even impossibility) of browsing all the operating points of the machine.

[0013] In fact, if all relevant data is available, a large amount of recording is necessary to ensure coverage of all operating points of the system. For example, for the motor temperature, the entire range of ambient temperatures that the motor will be subjected to (e.g., from 0°C to 40°C) should be recorded. If the entire range of ambient temperatures is not considered during the learning phase, the results of the virtual sensor in these missing learning areas will be incorrect or at least inaccurate.

[0014] Similarly, if the amount of relevant data is very large, it becomes obvious that learning cannot cover all operating points. For example, referring to Figure 1 the example of constructing a vibration virtual sensor on the lifting motion of a crane in the steel industry means considering all parameters that affect vibration, namely:

[0015] - Hoist speed

[0016] - Hoist acceleration

[0017] - Load

[0018] - Swing angle (i.e., the swing of the suspended load)

[0019] - Angular velocity of the swing

[0020] - Rotation speed

[0021] - Trolley speed

[0022] - Mast speed.

[0023] This results in a very high computational cost and places a heavy burden on the crane operator to browse the entire range of possible values of all eight parameters. In practice, such an exhaustive learning phase is not feasible.

[0024] A practical solution to reduce the number of operating points is to reduce the amount of relevant data by restricting the movement of the lifting appliance. In the previous example of a vibration virtual sensor for a crane lifting movement in the steel industry, to allow effective predictive maintenance, all other movements that may interfere with the vibration of the lifting motor can be prohibited. In this way, it will be possible to develop a vibration virtual sensor that will only consider the operating parameters of the lifting motor and will provide an accurate prediction of its health status as long as its operation is isolated from other prohibited movements of the crane.

[0025] The drawback of this solution is that prohibiting the combined movement of different movable parts of the lifting appliance reduces the performance of the crane. This reduction in productivity will be directly caused by the additional constraints implied by the predictive maintenance function, which will hinder the development of a fully automated lifting system capable of automatically transferring a suspended load along a track.

[0026] Therefore, there is a need for a method for operating a lifting appliance that provides high-performance production and effective predictive maintenance results. Summary of the Invention

[0027] The present disclosure improves this situation.

[0028] A method for operating a lifting appliance spanning a lifting area is proposed, the lifting appliance including N≥2 movable parts for transporting a load from a starting point to a destination, the N movable parts being configured for linear movement along any one of three orthogonal axes X, Y, and Z or for angular movement, the method comprising selecting, in a control device, speed parameters for the displacements of the N movable parts for transporting the load from the starting point to the destination by:

[0029] Determining a set of speed parameters for the displacements of the N movable parts belonging to an operating area for which the predictive maintenance function of the lifting appliance produces results above a determined accuracy threshold;

[0030] Select a speed parameter in the group that minimizes the travel time of the load from the starting point to the destination point.

[0031] On the other hand, a device for operating a lifting appliance across a lifting area is proposed, the lifting appliance comprising N≥2 movable parts for transporting a load from a starting point to a destination, the N movable parts being configured for linear or angular movement along any one of three orthogonal axes X, Y and Z, the device comprising:

[0032] One or more network interfaces for communicating with a communication network;

[0033] A memory configured to store code instructions executable by a processor;

[0034] A processor coupled to the network interface and configured to execute the code instructions stored in the memory to:

[0035] Select a speed parameter for the displacement of the N movable parts to transport the load from the starting point to the destination by:

[0036] Determine a set of speed parameters for the displacements of the N movable parts belonging to an operating area for which the predictive maintenance function of the lifting appliance produces results above a determined accuracy threshold;

[0037] Select a speed parameter in the group that minimizes the travel time of the load from the starting point to the destination point.

[0038] On the other hand, a computer software is proposed, which comprises instructions that, when executed by a processor, implement at least a part of the method defined herein. On the other hand, a computer-readable non-transitory recording medium is proposed, on which the software is registered, which, when executed by a processor, implements the method defined herein.

[0039] The following features can be optionally implemented individually or in combination with other features:

[0040] The method further comprises a preliminary learning phase of creating a digital twin model of the lifting appliance, the model being provided by a point cloud collected during the normal operation of the lifting appliance, the point cloud comprising operating points defined as N sets of values of speed parameters for each of the N movable parts, and the operating area being defined as an area where the point cloud density is greater than a predetermined threshold.

[0041] Determining a set of speed parameters for the displacements of the N movable parts comprises:

[0042] Assigning a high priority to one of the movable parts, called the reference movable part,

[0043] For each other movable part:

[0044] Determine a speed synchronization curve that plots the speed of the other movable part as a function of the speed of the reference movable part such that the other movable part and the reference movable part terminate motion simultaneously to transport a load from the starting point to the destination point,

[0045] Determine a set of significant operating points as points that belong to both the speed synchronization curve and a 2D representation of the operating region in the plane of the speed synchronization curve.

[0046] Selecting a speed parameter in the set that minimizes the travel time of the load includes:

[0047] Select the operating speed of the reference movable part as the maximum speed belonging to the set of significant operating points determined for each other movable part,

[0048] For each other movable part, select the operating speed on the speed synchronization curve according to the selected operating speed of the reference movable part.

[0049] Assigning a high priority to one of the movable parts includes:

[0050] Calculate the minimum travel time for each movable part taking into account the maximum operating speed of the movable part and the distance the movable part has to travel to transport the load from the starting point to the destination point;

[0051] Select the movable part associated with the maximum calculated minimum travel time as the reference movable part, and the maximum calculated minimum travel time is called the reference travel time;

[0052] Determining the speed synchronization curve for each other movable part includes:

[0053] Calculate the minimum operating speed of the other movable part as the ratio of the distance the other movable part has to travel to the reference travel time;

[0054] For the other movable part, calculate the ratio of the minimum operating speed to the maximum operating speed, called the speed ratio;

[0055] Determine the speed synchronization curve that plots the speed of the movable part as a function of the speed of the reference movable part and has the speed ratio as the slope.

[0056] Select a speed parameter in the set of speed parameters only if the speed parameter allows the productivity of the lifting appliance to be greater than a determined threshold of productivity.

[0057] The lifting device includes a gantry and a hoisting carriage. The hoisting carriage is capable of transporting a load suspended on a lifting mechanism in the hoisting carriage. The gantry is capable of moving substantially horizontally along axis X, the hoisting carriage is capable of moving substantially horizontally along axis Y, the lifting mechanism is capable of moving substantially vertically along axis Z, and the lifting device includes a rotary tool capable of angular movement. Description of the Drawings

[0058] Other features, details and advantages will be shown in the following detailed description and the drawings, where:

[0059] Figure 1 An example of a lifting device is schematically shown.

[0060] Figure 2 An example of a communication system for operating a lifting device according to an embodiment is schematically shown.

[0061] Figure 3 is a flowchart showing a method for operating a lifting device to achieve reliable predictive maintenance according to an embodiment.

[0062] Figure 4 is a flowchart showing a method for performing predictive maintenance of a lifting device according to a method according to an embodiment Figure 3 of.

[0063] Figure 5A shows according to an embodiment Figure 1 a representation of the speed synchronization curve of the hoisting carriage of a lifting device of.

[0064] Figure 5B shows according to an embodiment Figure 1 a representation of the speed synchronization curve of the hoist of a lifting device of.

[0065] Figure 5C shows according to an embodiment Figure 1 a representation of the speed synchronization curve of the rotary tool of a lifting device of.

[0066] Figure 6A shows a 2D representation of a point cloud of operating points of the hoisting carriage speed and the gantry speed collected during the operation of the lifting device.

[0067] Figure 6B shows a 2D representation of a point cloud of operating points of the hoist speed and the gantry speed collected during the operation of the lifting device.

[0068] Figure 6C shows a 2D representation of a point cloud of operating points of the rotational speed and the gantry speed collected during the operation of the lifting device.

[0069] Figure 7AShows a representation of an operating area for predictive maintenance of the hoist speed and mast speed.

[0070] Figure 7B Shows a representation of an operating area for predictive maintenance of the elevator speed and mast speed.

[0071] Figure 7C Shows a representation of an operating area for predictive maintenance of the rotation speed and mast speed.

[0072] Figure 8A Shows Figure 7A the operating area of Figure 5A and a combined representation of the speed synchronization curve of

[0073] Figure 8B Shows Figure 7B the operating area of Figure 5B and a combined representation of the speed synchronization curve of

[0074] Figure 8C Shows Figure 7C the operating area of Figure 5C and a combined representation of the speed synchronization curve of Detailed Description

[0075] The accompanying drawings and the following description illustrate specific exemplary embodiments of the present disclosure. Accordingly, it should be understood that those skilled in the art will be able to design various arrangements that, although not explicitly described or shown herein, embody the principles of the present disclosure and are included within its scope. In addition, any examples described herein are intended to assist in understanding the principles of the present disclosure and should be construed as not being limited to these specifically recited examples and conditions. Therefore, the present disclosure is not limited to the specific embodiments or examples described below, but is defined by the claims and their equivalents.

[0076] Now refer to Figure 2 , Figure 2 which shows a communication system for operating a lifting appliance while allowing effective predictive maintenance. The communication system includes a control device CD, a set of metering devices MD, a digital twin DT, and a monitoring system SUP.

[0077] A lifting area such as a warehouse, yard, hall, or other work area is provided with a monitoring system SUP, which is an IT control system for monitoring the lifting area. The monitoring system provides information to the control device CD for trajectory execution, authorization (i.e., access management), and general security.

[0078] The control device CD can communicate with the monitoring system SUP, a set of metering devices MD, and the digital twin DT via a telecommunications network TN. The telecommunications network can be a wired or wireless network, or a combination of wired and wireless networks. The telecommunications network can be associated with a packet network, such as an IP ("Internet Protocol") high-speed network, such as the Internet or an intranet, or even a private network dedicated to a company. The control device CD can be a programmable logic controller (PLC) and other automation devices capable of implementing industrial processes and communicating with the monitoring system to exchange data (such as requests, inputs, control data, etc.).

[0079] In one embodiment, the set of metering devices MD includes devices for metering the operating parameters of the lifting appliance, such as vibration sensors, torque meters, temperature sensors, etc. of the lifting motor.

[0080] In one embodiment, the digital twin DT includes virtual sensors for predicting the physical values of the operating parameters of the lifting appliance. Preferably, each virtual sensor of the digital twin DT corresponds to a metering device MD, allowing the control device CD to compare the output of the digital twin DT with the observed physical values output by the set of metering devices MD.

[0081] The control device CD is configured to create the path followed by the crane to transport the load from one place within the lifting area to another.

[0082] The control device CD is configured to select speed parameters for any movable part of the lifting appliance, and these speed parameters meet the following common goals: minimizing the travel time along the created path and operating the lifting appliance in the operating area for which the predictive maintenance function provided by the digital twin DT produces results higher than a determined accuracy threshold. For example, the control device CD selects the values of the trolley speed, mast speed, hoist speed, and rotation speed in the lifting appliance and ensures that these values allow the lifting appliance to be operated in the operating area where the predictive maintenance function relying on the digital twin DT is reliable, while maximizing the production performance of the lifting appliance.

[0083] Reference Figure 3 , the method for operating a lifting appliance for transporting a load while jointly optimizing the production level and predictive maintenance of a crane according to an embodiment includes steps S1 to S3.

[0084] In step S1, the control device CD determines the operating area of the lifting appliance for which the predictive maintenance function relying on the digital twin DT provides reliable results. Such an operating area is an area including a sufficient number of known operating points of the lifting appliance that have been browsed during the learning phase for creating the digital twin DT. Operating the lifting appliance in these operating areas ensures obtaining effective predictive maintenance results.

[0085] In step S2, given a target request to transport a load along a path from a starting point to a destination point in a lifting area, the control device CD determines a set of speed parameters of the movable parts of the crane (e.g., trolley speed, mast speed, hoist speed, slewing speed) that allow responding to the request and belong to the operating area determined in step S1.

[0086] In step S3, the control device CD selects, among this set of speed parameters, the speed parameters of the movable parts of the crane that minimize the travel time of the load along the path and thus maximize the production performance of the crane.

[0087] As Figure 4 shown in the flowchart of, this method can be integrated into a method for performing predictive maintenance of a lifting appliance during its operation.

[0088] Initially, in step S01, the control device CD performs a learning phase during which the operating points of the lifting appliance are browsed during the normal operation of the crane: the relevant data is collected and recorded by the set of metering devices MD. In an embodiment, the data collected matches a point cloud including the operating points, where the operating points are defined as N sets of values of each speed parameter of the N movable parts of the lifting appliance (e.g., N = 4, and the N sets of values include trolley speed, mast speed, hoist speed, and slewing speed). This learning phase may occur over a time range of several days to several weeks of normal operation of the lifting appliance.

[0089] In step S02, a digital twin model is created to reproduce all or part of the normal behavior of the lifting appliance. For example, the digital twin model includes virtual vibration sensors for the lifting motors, virtual temperature sensors, and virtual torque sensors for predicting vibration, temperature, and torque values at the precise operating points of the lifting appliance (e.g., for a given speed of the trolley, mast, hoist, and slewing tool). Such a digital twin model relies on a mathematical model of the interaction between the lifting appliance and its movable parts and is known in the prior art. The digital twin DT can be stored by the control device CD or in a remote computing device communicating with the control device via a telecommunications network TN. The operations performed in steps S01 and S02 allow creating a predictive maintenance function for the lifting appliance.

[0090] Once steps S01 and S02 are completed, predictive maintenance of the lifting appliance can be implemented when the lifting appliance is operated by the control device CD.

[0091] As previously regarding Figure 3As described above, in step S1, based on the data collected during the learning phase S01 and the number of operating points browsed during this phase, the control device CD defines the operating area of the movable parts of the lifting appliance as the area where the point cloud density is greater than a predetermined threshold. These areas include "sufficient" known operating points (high density, above the threshold), which define the areas related to the predictive maintenance function. Areas that do not include sufficient known operating points (low density, below the threshold) are useless areas for the predictive maintenance function, or at least operating areas where the predictive maintenance function cannot achieve sufficiently reliable or accurate results.

[0092] For example, the area can be gridified (e.g., with 10% by 10% squares, i.e., 100 squares). When there are a sufficient number of points (e.g., 3) in a square, the square is included in the "known area". In an embodiment, the determination threshold of the point density can be set to 3 points % of the surface.

[0093] In step S2, the control device CD analyzes the travel path of the load from the starting point to the destination point and assigns priorities to different movements in the lifting appliance. For example, considering the maximum possible speed of each movable part in the crane, the movement with the maximum travel time from the starting point to the destination point is assigned the highest priority because it is the movement that limits the production performance of the crane. This path analysis allows the determination of a set of speed parameters that both allow the fulfillment of the target requests assigned to the lifting appliance and belong to the operating area;

[0094] In step S3, and as will be described in more detail below with respect to FIGS. 5a to 8c, the control device adjusts the movement and trajectory of the automatic crane to achieve the goals of predictive maintenance and production: optimizing the path according to the correct operating points determined by the control device CD.

[0095] In step S4, the set of metering devices MD measures data while the crane moves along the load path. Step S5 is the end of the movement test, which causes the continuous data measurement in step S4 if the lifting appliance is still moving to transport the load to its destination point, or in step S6 feeds the measured data to the digital twin DT if the load has reached its destination point.

[0096] In step S7, the control device CD compares the physical values predicted by the digital twin DT with the observed physical values measured by the set of metering devices MD. If a significant difference between the predicted value and the observed value is evaluated in step S8, this may be an indication of an anomaly, or a future defect or failure of a component. The control device CD issues an alarm in step S9, and the alarm can take the form of an alarm message sent to the monitoring system SUP. The alarm message can be displayed on the graphical user interface GUI to notify the operator of the lifting appliance that a maintenance intervention must be planned.

[0097] After step S9, or directly after step S8, in the case where the digital twin output matches the physical values observed by the set of metering devices MD, the control device CD prepares to execute a new target request in step S10 and creates a new path for the crane to follow to transport the load from a new starting point to a new destination within the lifting area.

[0098] Reference will now be made to Figures 5A to 8C describe in more detail the operations performed by the control device CD during steps S1 to S3, Figures 5A to 8C An exemplary embodiment of a lifting appliance having four movable parts is shown, namely a gantry, a trolley, a lifting mechanism, and a rotating tool. The movements of all four movable parts can be combined. According to the embodiment, the control device CD seeks the best solution for combining all four movements to achieve the best production performance of the lifting appliance and reliable measurements for predictive maintenance monitoring. A person skilled in the art will easily generalize this exemplary embodiment to a lifting appliance having N≥4 movable parts.

[0099] To achieve this goal, the control device must, on the one hand, analyze the path corresponding to the goal of the lifting appliance to maximize the production level (steps S2 and S3), and on the other hand, consider the operating area (step S1) in which monitoring the lifting appliance for predictive maintenance provides reliable results.

[0100] In the exemplary embodiment, the lifting appliance receives a request to transport a load from a starting point to a destination within the lifting area. The control device CD first calculates the distance that each movable part of the crane has to travel in response to this request. The following table provides an example of the moving distance and its maximum possible speed for each movable part.

[0101] Table 1

[0102] Travel distance Maximum speed Mast 50m 2 m / s Carriage 6m 1 m / s Hoist 5m 0.25 m / s Rotation 90° 6° / s

[0103] The data in Table 1 allows the calculation of the best operating time to achieve each requested action, as shown in the last column of Table 2 below.

[0104] Table 2

[0105] Travel distance Maximum speed Time Mast 50m 2 m / s 25s Carriage 6m 1 m / s 6s Hoist 5m 0.25 m / s 20s Rotation 90° 6° / s 15s

[0106] As can be observed, the most constrained time is associated with the gantry movement, since the gantry takes 25 s to travel from its starting point to its destination and will therefore be the last movable part to complete its movement. In this exemplary embodiment, the gantry movement is therefore assigned the highest priority and is selected as the reference movement. The gantry is the reference movable part.

[0107] This 25 s travel time results in the maximum production performance level of the lifting appliance, which can be denoted as t_prod_max. It allows calculating the minimum speed of each other movable part (transfer cart, hoist, rotation), below which the maximum production performance level t_prod_max will decrease. For each movable part, the minimum speed is calculated as follows: min_speed = distance / t_prod_max. Table 3 provides the minimum speeds calculated for each movable part.

[0108] Table 3

[0109] Travel distance Maximum speed Time Minimum speed Mast 50m 2 m / s 25s / Carriage 6m 1 m / s 6s 0.24 m / s Hoist 5m 0.25 m / s 20s 0.2 m / s Rotation 90° 6° / s 15s 3.6° / s

[0110] Then, the ratio of this minimum speed to the maximum possible speed can be calculated for each movable part: Ratio = min_speed / max_speed. The results are recorded in Table 4 below.

[0111] Table 4

[0112] Travel distance Maximum speed Time Minimum speed Ratio Mast 50m 2 m / s 25s / / Carriage 6m 1 m / s 6s 0.24 m / s 24% Hoist 5m 0.25 m / s 20s 0.2 m / s 80% Rotation 90° 6° / s 15s 3.6° / s 60%

[0113] The calculated ratios allow deriving the speed synchronization curves for each movable part (transfer cart, hoist, rotation) as a function of the speed of the reference movable part, i.e., the speed of the gantry, such that all four motions (along the X, Y, Z axes and angular movement) terminate simultaneously. These speed synchronization curves are shown as dashed lines in Figures 5A to 5C for the transfer cart ( Figure 5A ), the hoist ( Figure 5B ), and the rotation ( Figure 5C ).

[0114] When looking at Figures 5A to 5C it can be understood that if the gantry travels at 100% of its maximum speed, while the transfer cart travels at 24% of its maximum speed, the crane travels at 80% of its maximum speed, and the rotary tool travels at 60% of its maximum speed, the lifting appliance will take 25 s to achieve its target request. Therefore, as long as the transfer cart speed is greater than 24% of its maximum speed, the hoist speed is greater than 80% of its maximum speed, and the rotation speed is greater than 60% of its maximum speed, the target request achievement time will also be 25 s. However, if the transfer cart speed is less than 24% of its maximum speed, or the hoist speed is less than 80% of its maximum speed, or the rotation speed is less than 60% of its maximum speed, the target request achievement time will be greater than 25 s.

[0115] In other words, if for any reason the gantry speed should be reduced, for example, to 50% of its maximum speed, it is possible to reduce the speed of the other three movable components at the same rate without reducing the crane's production performance more severely: the travel time of the lifting appliance will be 50 s. Figures 5A to 5C Points 51 to 53 on Figures 5A to 5C show the possible speed parameters of the trolley, hoist, and slewing tool for optimizing the crane's production performance in the case where the gantry speed is reduced to 50% of its maximum speed. Thus, a set of significant operating points can be defined for each movable component along the dashed line of the speed synchronization curve. The speed parameters of each movable component must be selected from this set of speed parameters to minimize the travel time of the lifting appliance and thus optimize its production performance level.

[0116] In addition, these speed parameters must also be selected to enable reliable predictive maintenance of the lifting appliance.

[0117] The operating area of the lifting appliance must be determined in which the results generated by the predictive maintenance function are above a determined accuracy or reliability threshold (step S1).

[0118] Figures 6A to 6C Shows a 2D representation of the point cloud of the operating points that have been traversed during the learning phase S01. Each point in such a point cloud can be represented as a vector comprising four components, namely the gantry speed, the trolley speed, the hoist speed, and the slewing speed. Figure 6A Shows a 2D representation of this point cloud in the plane defined by the gantry speed on the X-axis and the trolley speed on the Y-axis. Figure 6B Shows a 2D representation of this point cloud in the plane defined by the gantry speed on the X-axis and the hoist speed on the Y-axis. Figure 6C Shows a 2D representation of this point cloud in the plane defined by the gantry speed on the X-axis and the slewing speed on the Y-axis.

[0119] When observing Figures 6A to 6C the 2D representation of the operating point cloud on Figures 6A to 6C , regions can be identified where the density of the points is greater than a determined density threshold and where sufficient data has thus been recorded during the learning phase S01 to enable reliable predictive maintenance of the crane. Figures 7A to 7C Shows grey regions, called regions 71 to 73, where the density of the known operating points is greater than 3 percentage points of the surface and where the predictive maintenance function of the lifting appliance can thus provide reliable results.

[0120] As Figures 8A to 8C shown, Figures 5A to 5C the speed synchronization curve of Figures 5A to 5C can be superimposed on Figures 7A to 7Con the operating areas of each movable part of the lifting appliance. For each movable part, a set of significant operating points can be identified, which belong to both the speed synchronization curve and the operating area for reliable predictive maintenance. In Figures 8A to 8C these points are shown as dots. Operating points that belong to the speed synchronization curve but are outside the operating area for reliable predictive maintenance (i.e., outside areas 71, 72, or 73) are represented by crosses.

[0121] In this exemplary embodiment, a set of significant operating points for the trolley corresponds to a range of trolley speed values that includes between 0% and 90% of the mast speed, as Figure 8A shown. The set of significant operating points for the lifting mechanism corresponds to a range of hoist speed values that includes between 0% and 75% of the mast speed, as Figure 8B shown. The set of significant operating points for the rotary tool corresponds to a range of rotational speed values that includes between 0% and 15% or between 50% and 100% of the mast speed, as Figure 8C shown.

[0122] The control device CD determines the best solution for achieving the target request by selecting the best percentage of the mast speed for which the corresponding speeds of the other movable parts of the lifting appliance belong to Figures 8A to 8C a set of significant operating points shown as dots in

[0123] Table 5

[0124] Travel distance Maximum speed Ratio of maximum speed Operating speed Travel time Mast 50m 2 m / s 75% 1.5 m / s 33.3s Carriage 6m 1 m / s 18% 0.18 m / s 33.3s Hoist 5m 0.25 m / s 60% 0.15 m / s 33.3s Rotation 90° 6° / s 45% 2.7° / s 33.3s

[0125] In this exemplary embodiment, the best time for achieving the target request of the lifting appliance while allowing reliable monitoring of the crane is 33.3 s. Thus, compared to the maximum production level t_prod_max achieved with a travel time of 25 s, the production performance level of the lifting appliance is reduced by 33%. However, it allows reliable prediction of the health state of the lifting appliance, which in the long run ensures a satisfactory production performance level by avoiding unexpected interruptions in machine operation.

[0126] However, in an embodiment, a maximum threshold for a reduction in production level can be set, and the control device CD cannot be lower than this threshold. This threshold can be adjusted, for example, by an operator of the crane depending on the production constraints of the lifting appliance and can vary over time or operating cycles. For example, the operator can decide that the production performance level should not be less than 60% of the maximum production level t_prod_max. In the above exemplary embodiment, this means that the maximum time for the lifting appliance to achieve the request should not be greater than 35 s.

[0127] If the control device CD cannot determine the speed parameters of the mast, trolley, lifting mechanism, and rotating tool that satisfy this constraint, it can decide to operate the lifting appliance at its maximum speed and abandon its monitoring by using the digital twin. In this case, it can issue an alarm, which can be sent to the monitoring system SUP. The alarm message can be displayed on the graphical user interface GUI to notify the operator of the lifting appliance that the predictive maintenance function is deactivated.

[0128] The embodiment includes a control device CD in the form of a device that includes one or more processor I / O interfaces and a memory coupled to the processor. The processor can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operation instructions. The processor can be a single processing unit or multiple units, all of which can also include multiple computing units. Among other capabilities, the processor is configured to obtain and execute computer-readable instructions stored in the memory.

[0129] The functions implemented by the processor can be provided by using dedicated hardware as well as hardware capable of executing software associated with the appropriate software. When provided by the processor, these functions can be provided by a single dedicated processor, a single shared processor, or multiple separate processors, some of which can be shared. In addition, the explicit use of the term "processor" should not be construed as specifically referring to hardware capable of executing software and can implicitly include, but is not limited to, digital signal processor (DSP) hardware, network processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile memory. Other conventional and / or custom hardware can also be included.

[0130] The memory may include any computer-readable medium known in the art, including, for example, volatile memory such as static random access memory (SRAM) and dynamic random access memory (DRAM), and / or non-volatile memory such as read-only memory (ROM, erasable programmable ROM, flash memory, hard disk, optical disk, and magnetic tape). The memory includes modules and data. The modules include routines, programs, objects, components, data structures, etc., which perform specific tasks or implement specific abstract data types. Among them, the data is used as a repository for storing data processed, received, and generated by one or more modules.

[0131] Those skilled in the art will readily recognize that the steps of the above method can be performed by a programmed computer. Here, some embodiments also aim to cover program storage devices, such as digital data storage media, which are machine or computer-readable and encode a program of machine-executable or computer-executable instructions, where the instructions perform some or all of the steps of the method. The program storage device may be, for example, a digital memory, a magnetic storage medium such as a disk and magnetic tape, a hard disk drive, or an optically readable digital data storage medium.

Claims

1. A method for operating a lifting device (1), the lifting device (1) spanning a lifting area, the lifting device comprising N ≥ 2 movable parts (2, 3, 4), for transporting a load (6) from a starting point to a destination point, the N movable parts being configured for linear movement along any one of three orthogonal axes X, Y and Z or for angular movement, The method comprises selecting in a control device (CD) a velocity parameter for the displacement of the N movable parts to transport the load from the starting point to the destination point by: determining (S2) a set of velocity parameters of the displacement of the N movable parts belonging to an operating area (71, 72, 73) for which a predictive maintenance function of the lifting appliance produces a result above a determined accuracy threshold; A speed parameter that minimizes the travel time of the load from the starting point to the destination point is selected (S3) from the set of speed parameters.

2. The method according to claim 1, further comprising a preliminary learning phase (S01) to create a digital twin (DT) model of the lifting appliance, the model being provided by a point cloud collected during normal operation of the lifting appliance, the point cloud comprising operating points defined as N sets of values ​​for each speed parameter of N movable parts, wherein the operating area (71, 72, 73) is defined as an area where the density of the point cloud is greater than a predetermined threshold.

3. The method according to claim 1 or 2, wherein a set of velocity parameters for determining the displacement of the N movable components comprises: assigning a high priority to one of the movable parts, referred to as a reference movable part, For each additional movable part: determining a speed synchronization curve that plots the speed of the other movable part as a function of the speed of the reference movable part so that the other movable part and the reference movable part terminate motion simultaneously to transport a load from the starting point to the destination point, determining a set of significant operating points as points belonging both to the speed synchronization curve and to a 2D representation of the operating region in the plane of the speed synchronization curve, Wherein the speed parameter selected from the set of speed parameters to minimize the travel time of the load comprises: selecting the operating speed of the reference movable part as the maximum speed belonging to a set of significant operating points determined for each of the other movable parts, For each of the other movable parts, an operating speed on the speed synchronization curve is selected according to the operating speed selected for the reference movable part.

4. The method of claim 3, wherein assigning a high priority to one of the movable components comprises: calculating a minimum travel time for each movable component taking into account a maximum operating speed of the movable component and a distance to be traveled by the movable component in order to transport the load from the origin to the destination point; selecting the movable component associated with the largest calculated minimum travel time as a reference movable component, the largest calculated minimum travel time being referred to as the reference travel time; and wherein determining the speed synchronization curve of each other movable component comprises: calculating a minimum operating speed of the other movable component as a ratio of a distance to be traveled by the other movable component to the reference travel time; calculating a ratio of said minimum operating speed to said maximum operating speed for said other movable components, referred to as a speed ratio; A speed synchronization curve is determined that plots the speed of the movable member as a function of the speed of the reference movable member, the speed synchronization curve having the speed ratio as a slope coefficient.

5. Method according to any of the preceding claims, wherein said speed parameter is selected in said set of speed parameters only if said speed parameter allows to achieve a productivity of said lifting appliance greater than a determined threshold of productivity.

6. A method according to any one of the preceding claims, wherein the lifting device (1) comprises a gantry (3) and a trolley (2), the trolley being capable of transporting a load (6) suspended on a lifting mechanism (4) in the trolley, wherein the gantry is capable of moving substantially horizontally along an axis X, the trolley is capable of moving substantially horizontally along an axis Y, the lifting mechanism is capable of moving substantially vertically along an axis Z, and wherein the lifting device comprises a rotating tool capable of angular movement.

7. An apparatus for operating a lifting device, the lifting device spanning a lifting area, the lifting device comprising N ≥ 2 movable parts, for transporting a load from a starting point to a destination point, the N movable parts being configured for linear or angular movement along any one of three orthogonal axes X, Y and Z, the apparatus comprising: one or more network interfaces for communicating with a communication network; a memory configured to store code instructions executed by the processor; a processor coupled to the network interface and configured to execute code instructions stored in the memory to: The velocity parameters for the displacement of the N movable components to transport the load from the starting point to the destination point are selected in the following manner: determining a set of velocity parameters of the displacements of the N movable components belonging to an operating area for which a predictive maintenance function of the lifting appliance produces results above a determined accuracy threshold; A speed parameter is selected from the set of speed parameters that minimizes a travel time of the load from the origin to the destination point.

8. The device according to claim 7, wherein the lifting device comprises a gantry and a trolley, the trolley being capable of transporting a load suspended on a lifting mechanism in the trolley, wherein the gantry is capable of moving substantially horizontally along an axis X, the trolley is capable of moving substantially horizontally along an axis Y, the lifting mechanism is capable of moving substantially vertically along an axis Z, the lifting device comprising a rotating tool capable of angular movement, And wherein the processor is configured to execute code instructions stored in the memory to select velocity parameters for displacement of the gantry, the trolley, the lifting mechanism, and the rotary tool, which achieves a joint objective.

9. The apparatus according to claim 7 or 8, wherein the processor is configured to execute code instructions stored in the memory to perform the method according to any one of claims 1 to 6.

10. Computer software comprising instructions which, when executed by a processor, implement a method for operating a lifting appliance according to any one of claims 1 to 6.

11. A non-transitory machine-readable storage medium encoded with instructions to be executed by a processor, the non-transitory machine-readable storage medium comprising instructions for implementing the method for operating a lifting appliance according to any one of claims 1 to 6.

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