Method for diagnosing crane activity to determine anomalies causing a decrease in activity

Abstract

A diagnostic method for detecting and classifying a period of reduced activity of a crane (2) on a construction site among several periods of activity, which includes at least the following steps: - detection of crane data (D1, D11, D12) from its equipment (3, 31) which includes in particular work data (DW) representative of a crane operation; and environmental data (D2) representative of the environment of the construction site; - for each period of activity, processing of the work data to determine whether the period of activity is a period of reduced activity or not, including an analysis of the crane's working time during this period; - for each period of reduced activity, processing of the crane and environmental data associated at least with said period of reduced activity to identify at least one anomaly explaining the period of reduced activity.Figure from the summary: Figure 2.

Description

Title of the invention: Method for diagnosing crane activity to determine anomalies causing a drop in activity Technical field

[0001] The invention relates to a method for diagnosing a crane for evaluating its activity when used on a construction site.

[0002] It relates more particularly to a diagnostic method in which data relating to the crane and its environment are analyzed, making it possible to determine over time periods where the productivity / activity of the crane would have decreased, and to identify the causes or anomalies at the origin of this decrease in activity.

[0003] The invention thus finds a preferred application in the management of a construction project and the organization, both technical, human and material, of a construction site in which one or more cranes are used. Prior art

[0004] As is known, understanding and analyzing the causes of schedule delays and reduced productivity on a construction site can prove particularly complex due to the plurality of human and material actors that may be involved.

[0005] The decreases in productivity / activity during the day of a crane used on this site can have many sources, both internal and external to the crane. For example, a delay in a logistics operation or an extreme climatic condition can result in the crane being shut down, regardless of its proper functioning. There are also other causes of a drop in activity such as, for example, but not limited to: a breakdown due to a malfunction / stoppage of crane equipment; a poorly optimized crane load plan (times when the crane is working, times when it is inactive, etc.); human error in its assembly, adjustment or handling, etc.

[0006] It is also known that a crane is equipped with a plurality of equipment necessary for its control and operation; and also sensors providing various information on the crane itself or its environment, for example and not exhaustively: a lifting speed when the crane lifts a load, a rotation speed of a boom when it moves the load, alerts when the boom of the crane risks colliding with the boom of a second crane, a number of starts and / or stops, a state of the equipment, etc.

[0007] In the literature, solutions like those presented in the documents US20120158279 and US20180018641 propose to exploit the data provided by the equipment of a machine, such as a crane, in order to provide performance indicators on it or to ensure predictive maintenance (by predicting the lifespan of the components in order to carry out actions following this, such as renewing a component if it is at the end of its life). Documents CN107025521 and DE102015006992 propose to exploit equivalent data in order respectively to provide an optimized work plan to the crane, and to optimize the capacities of the crane according to the intensities of its requests to avoid any overload situation.

[0008] On the other hand, none of these solutions propose to exploit the data provided by the crane in order to contextualize the drops in crane activity and their repercussions on the general progress schedule of the site, both in terms of material and human resources. Summary of the invention

[0009] In order to address the problem set out, the invention relates to a method for diagnosing the activity of a crane for detecting and classifying a period of reduced activity of said crane on a construction site among several periods of activity, said diagnostic method implementing at least the following steps: - detection of crane data from crane equipment, and comprising at least work data representative of crane work implementing at least one maneuver of at least one structural element of the crane; - detection of environmental data representative of a construction site environment; - historization by activity period of crane data and environmental data in a remote database; - for each period of activity, processing of the work data to calculate a working time of the crane during the period of activity, and comparison of said working time with at least one activity threshold to determine whether said period of activity is a period of reduced activity or not; - for each period of reduced activity, processing of crane data and environmental data associated at least with said period of reduced activity to identify at least one anomaly, of the construction site or of the crane, with which said period of reduced activity is associated.

[0010] In other words, and advantageously, from: - data provided by the equipment included in the crane, and which are at least representative of the work of the latter when at least one of its elements structural is maneuvered (for example the block which is raised when a load is lifted, or the boom which rotates to move the load after it has been lifted), and - environmental data representative of the environment and the location of the construction site in which the crane is operating, which data is collected during one or more given activity periods (an activity period may be, for example, a day, a week, a month) then stored and historicized by activity period (so that the data collected specific to each activity period can be identified) in the remote database,

[0011] the diagnostic method is capable of: - to determine whether or not a drop in crane activity has occurred for at least one period of activity considered. To do this, the method calculates, from data representative of the crane's work, its activity time during the period of activity, i.e. the total duration during this period of activity during which it was active; then compares this activity time to an activity threshold which may, for example, correspond to an average activity time objectified by the construction site managers; and - in the case of a detected drop in activity, to determine and contextualize the causes, subsequently designated as anomalies, at the origin of this drop in activity, then allowing site managers to continuously improve the progress schedule of the site on the material (with better management of equipment for example) and / or human and / or logistical / organizational levels.

[0012] According to a characteristic of the invention, the at least one anomaly comprises at least one internal anomaly of the crane reflecting a technical failure of the crane and identified from the crane data.

[0013] According to one embodiment of the invention, the at least one internal anomaly comprises at least one hardware, software or communication fault of one of the pieces of equipment called faulty equipment, identified from crane data from said faulty equipment.

[0014] In other words, the diagnostic method is designed to determine whether the anomaly responsible for a drop in activity of the crane during a given period of activity is caused by a technical failure of the latter which may be caused, but not limited to: a malfunction or breakdown of one or more of these components and equipment; a malfunction or breakdown of software, a communication problem between equipment / components of the crane, etc. This type of anomaly is determined by the diagnostic method from data provided, for example, by the equipment / components of the crane themselves, or by the management tionary of its breakdown.

[0015] Advantageously, the identification of this anomaly allows the site manager to determine that the cause of the drop in crane activity comes from a failure of equipment on the machine itself, due for example to its lack of maintenance, a repair not carried out, or wear in the context of consumable equipment.

[0016] According to a characteristic of the invention, the at least one anomaly comprises at least one usage anomaly reflecting non-compliant use of the crane and identified from the crane data.

[0017] According to one embodiment of the invention, the at least one usage anomaly comprises at least one assembly anomaly reflecting non-compliant assembly or adjustment of the crane during its installation, and identified from sensor data chosen from the crane data and originating from at least one sensor of the crane.

[0018] According to one embodiment of the invention, the at least one usage anomaly comprises at least one piloting anomaly reflecting non-compliant piloting of the crane by a crane operator during maneuvers, and identified from the work data, such as for example speed data of at least one structural element of the crane or overload data.

[0019] In other words, the diagnostic method is designed to determine whether the anomaly responsible for a drop in crane activity during a given period of activity is caused by an anomaly in the use of the crane, not exhaustively: - improper control of the crane by the crane operator (lifting / dropping a load too quickly, rotating the boom too quickly, etc.); - improper installation of the crane, and / or adjustment of its structural, functional or ballast elements. For example, in order to meet specific needs of the construction site, specific adjustments must be made to the crane's elements and equipment to perform additional functions. However, these adjustments may not have been anticipated before the start of the construction site, meaning that delays will occur at unwanted times during the construction site's life cycle to make these adjustments, without which the site will not be able to progress.

[0020] Delays can also be caused by an unforeseen change in the initial configuration of the construction site, with the emergence of new, previously unknown needs.

[0021] This type of anomaly is determined by the diagnostic method from crane data provided for example by equipment / components or sensors of the crane, for example: data on the location of structural, functional and ballast elements; load / overload data when lifting a load; speed data when rotating the boom, or speed or positioning data when raising or lowering the block, etc.

[0022] Advantageously, the identification of this anomaly allows the site manager to determine that the cause of the drop in crane activity is not linked to the construction machine itself but to human errors, which should push the site manager to improve the organization of the site on a human level.

[0023] According to a characteristic of the invention, the at least one anomaly comprises at least one climatic anomaly reflecting an extreme climatic condition and identified from climatic data chosen from the environmental data.

[0024] According to a characteristic of the invention, the climatic data comprise at least one of the following data: temperature data, wind speed data and hygrometric data.

[0025] In other words, the diagnostic method is designed to determine whether the anomaly responsible for a drop in crane activity during a given activity period is caused by extreme climatic conditions, generally associated with bad weather, such as: very high or very low outside temperatures; very heavy rain; very strong gusts of wind. This type of anomaly is determined by the diagnostic method from environmental data such as temperature data, wind speed data and hygrometric data, which are provided by suitable measuring devices (such as outside temperature sensors, anemometers for measuring wind speed, etc.).

[0026] Advantageously, the identification of this anomaly allows the site manager to determine that the cause of the drop in crane activity is independent of the organization of the site (whether on the material, human, or logistical level), and to adapt the activity of the site, and therefore of the crane, accordingly, in particular if these climatic anomalies are recurrent: reduction of the activity of site workers during periods of high temperatures to prevent them from having an impact on their condition / health, weathervaning of the cranes if there is a risk of strong gusts of wind, etc.

[0027] According to a characteristic of the invention, the at least one anomaly comprises at least one organizational anomaly reflecting low profitability of the use of the crane and identified from the crane data.

[0028] According to one embodiment of the invention, the at least one organizational anomaly is identified from at least one of the following data among the crane data: data representative of a presence or activity of the crane operator in the crane, maneuver counting data, data representative of a stop commanded by an anti-collision system, counting data load lifting cycles, data representative of pause times between two maneuvers, data representative of types of maneuver, data representative of a type of crane.

[0029] In other words, the diagnostic method is designed to determine whether the anomaly responsible for a drop in crane activity during a given period of activity is caused by a logistical problem) or an organizational failure in the management of the construction site. The organizational failure can be expressed, for example, by a crane that is inactive most of the time during the period of activity and has had very little, or even no, load to lift or move in the worst case. This type of anomaly is determined non-exhaustively from data representative of the presence or activity of a crane operator in the crane (the non-presence of a crane operator in the crane meaning that it is inactive, or if he is present and performs few maneuvers, the pause time between each maneuver is significant), load lifting cycle counting data, etc.The type of crane is also an important piece of data because the usage benchmarks are not the same between the types of crane and the construction site environment context (rapid assembly crane for individual constructions, element-based assembly crane or automated assembly crane for building construction, etc.). Data representative of a stop controlled by an anti-collision system are sources of information concerning construction sites comprising several cranes, in particular if the circular working areas of the cranes overlap: they make it possible to determine whether there is a risk of collision between the crane booms or whether the crane stops are too frequent, meaning that the cranes hinder each other when carrying out their respective tasks.

[0030] Advantageously, the identification of this anomaly allows the site manager to determine that the cause of the drop in crane activity is linked to a logistical problem (which may or may not be the repercussion of another anomaly) or a poorly optimized organization of the site activity, allowing him to then propose suitable solutions. For example, in an application context in which a construction site would include several cranes, one of which would be inactive most of the time, a possible solution would be a reallocation of tasks between the cranes, if the risks of interference between the cranes remain low, and if this does not significantly disrupt the progress schedule of the site.

[0031] Similar to the first processing carried out to determine whether or not a period of activity corresponds to a period of decline in activity, the processing implemented to identify the type(s) of anomaly (internal anomaly, usage anomaly, climatic anomaly, organizational anomaly) explaining the decline in activity consist first of all in applying mathematical algorithms to the crane data and environmental data associated with this period of activity, which compare them to decision criteria perceptible / understandable by the analysts analyzing the period of activity. Depending on the results of these comparisons, classification / categorization algorithms classify / range the period of reduced activity in a category corresponding to one of the four types of anomaly cited, and also in a subcategory among several subcategories that the type (or category) of anomaly includes. These subcategories correspond to the various events causing the anomaly.For example, and with reference to the explanations given previously, at least three subcategories are included in the category related to the climatic anomaly; these three subcategories corresponding respectively to: extreme temperatures (high or low); heavy precipitation; and strong gusts of wind.

[0032] According to a characteristic of the invention, a remote analysis system, in communication with or comprising the remote database, implements the processing of the work data, the crane data and the environmental data to determine whether each period of activity is a period of reduced activity or not and to associate with each period of reduced activity the at least one corresponding anomaly.

[0033] In other words, the steps of processing the data associated with the at least one period of activity to identify whether the at least one period of activity is a period of decreased activity or not, and determining the type or types of anomaly explaining this decrease in activity are carried out by a remote analysis system in communication with the remote database; this remote analysis system may be, for example, a laptop or desktop computer.

[0034] According to one embodiment of the invention, the remote database is included in the remote analysis system.

[0035] According to one embodiment of the invention, all or part of the processing is carried out by the remote analysis system directly in the remote database.

[0036] According to one embodiment of the invention, the analysis system exports from the remote database the work data, the crane data and the environmental data of the at least one period of activity considered to carry out the processing.

[0037] According to a characteristic of the invention, the remote analysis system structures the crane data and the environmental data in the same predefined format.

[0038] Since work data, crane data and environmental data come from different equipment, these data can be in different formats. For each activity period, before the data associated with each activity period is imported into the database remote, they are, once collected, transmitted to the remote analysis system which will clean them and structure them according to a predefined format; with the aim of being more easily interpretable by the remote analysis system when it subsequently carries out processing on the data in order to determine periods of decline in activity and their origins.

[0039] According to one embodiment of the invention, the diagnostic method implements, in parallel with the processing of the crane data and the environmental data for each period of reduced activity, a display of a progress of said data processing for each period of reduced activity.

[0040] According to a characteristic of the invention, the diagnostic method implements, after processing the crane data and the environmental data of each period of reduced activity, a generation and / or a display of an analysis report comprising, for the or each period of reduced activity, information specific to the at least one identified anomaly.

[0041] This generation and / or this display may take the form of a visual representation specific to the at least one identified anomaly; and / or a description of a set of processing steps carried out during the processing of the crane data and the environmental data which led to the identification of the at least one anomaly.

[0042] In other words, with the aim that the results of the processing operations can be easily interpreted and understood by a person having implemented the diagnostic method in order to determine whether a crane on the construction site has experienced a drop in activity during a period of activity and, if so, to determine the causes which are at the origin thereof, the diagnostic method, according to different embodiments, can, following the processing of the crane data and the environmental data, generate a display report in a given format which can be consulted later, and / or display this analysis report on a screen. In different variant embodiments of the invention, the analysis report can comprise: - a visual representation of at least one type / category of anomaly identified for each of the activity periods that were the subject of the diagnosis, for example: a color, text giving the name of the anomaly and associated with a color, etc. By extension, it is possible that each subcategory of a type / category of anomaly also has its own visual representation to improve understanding of the results of the processing; and / or - for the at least one anomaly identified for each period of reduced activity, a detail of the processing carried out on the crane data, the work data, and the environmental data which led the diagnostic process to identify the at least one anomaly. By extension, it is possible that each subcategory included in the type / category of anomaly is accompanied by a description of the treatments that specifically led the diagnostic process to identify this subcategory.

[0043] According to a characteristic of the invention, the diagnostic method comprises at least one definition of an additional anomaly and at least one decision criterion for identifying the additional anomaly when processing the crane data and the environmental data.

[0044] Advantageously, the diagnostic method can be enriched and improved by informing / defining new anomalies, i.e. those which have never been encountered on the construction site. The definition of the new anomaly consists at least in informing at least one decision criterion which will be applied by the diagnostic method during the processing of the crane data and the environmental data so that it identifies that the drop in activity is due to this new type of anomaly. By extension, the diagnostic method can also be enriched and improved by defining for this new type of anomaly subcategories representative of the causes generating it. Here again, for each subcategory, at least one decision criterion must be defined so that the diagnostic method succeeds in identifying it during processing.Eventually, the definition of a new anomaly may require a change in the format used to structure the data, in the case where the anomaly is determined from data which are emitted by one or more pieces of equipment in a format not currently taken into account for the structuring of all the data (work data, crane data, environmental data) in a single format.

[0045] In a first variant embodiment of the invention, the anomaly definition additions are made from the remote analysis system by the users themselves.

[0046] In a second variant embodiment of the invention, the additions of anomaly definitions are made by the designers of the diagnostic method on the basis of customer feedback; the additions of anomaly definitions are then part of an agile development approach for continuous improvement of the diagnostic method. It is conceivable that the new anomaly definitions may be contained in updates of the diagnostic method available for downloading from the remote analysis system.

[0047] As regards the activity threshold, it can be fixed (and therefore be identical for all periods of activity) or it can be variable depending on the period of activity and / or the type of site activity and / or the type of crane.

[0048] Advantageously, for each period of activity, the activity threshold for said period of activity corresponds to an average value of the working times of several periods of activity prior to and / or subsequent to said period of activity.

[0049] Thus, the activity times of the previous and / or subsequent periods are taken into consideration in order to set the activity threshold for the period analyzed. Brief description of the drawings

[0050] Other characteristics and advantages of the present invention will appear on reading the detailed description below, of a non-limiting example of implementation, made with reference to the appended figures in which:

[0051] [Fig-1] is a flowchart illustrating a diagnostic method of the invention;

[0052] [Fig.2] is a schematic view of an implementation of the diagnostic method in the framework of determining one or more periods of reduced activity among several periods of activity of a distributing jib crane, by means of the crane's equipment and sensors, as well as an external system, transmitting crane data and environmental data to a remote analysis system which will process them for the purpose of said determination of periods of reduced activity;

[0053] [Fig.3] illustrates a first example of application of the diagnostic method for identifying and contextualizing periods of decreased activity among several periods of activity; a variable activity threshold being used by the diagnostic method during a first processing to identify the periods of decreased activity, and a first decision criterion being used during a second processing to determine whether the periods of decreased activity are partly due to a material defect / malfunction of equipment contributing to the lifting of a load by the crane;

[0054] [Fig.4] illustrates a second example of application of the diagnostic method for identifying and contextualizing periods of decreased activity among several periods of activity; a fixed activity threshold being used by the diagnostic method during the first processing to identify the periods of decreased activity, and a second decision criterion being used during the second processing to determine whether the periods of decreased activity are partly due to a material defect / malfunction of equipment contributing to the lifting of a load by the crane;

[0055] [Fig.5] is an illustration of an example of the type of graph that can be displayed on a screen connected to the remote analysis system and acting as an analysis report following the processing carried out on the crane data and the work data with a view to determining one or more periods of reduced activity; the graph corresponding to a hierarchical sunburst graph (a) representative of a period of reduced activity for which the inner ring pieces (respectively the outer ring pieces) correspond to the anomalies (respectively to the anomaly subcategories associated with the anomalies) defined in the diagnostic process; with here two examples of suspect weeks such that for one of them all the anomalies and subcategories were observed of anomaly defined (b), and for the other some of the anomalies and subcategories of anomaly among the set of anomalies and subcategories of anomaly defined (c).

[0056] [Detailed description of one or more embodiments of the invention]

[0057] With reference to [Fig.l] to [Fig.4], the invention relates to a method 1 for diagnosing an activity of a crane 2 in front, based on an analysis of a plurality of data relating to the crane 2 and its environment: - determining whether or not a decrease in activity of crane 2 occurred during a given AP activity period, an AP activity period being, for example, a day, a week, a month. An AP activity period during which a decrease in activity was observed is called a LAP activity decrease period; - in the event that a period of reduced activity has been detected, determine and contextualize the causes, subsequently designated as anomalies A1, A2, A3, A4 at the origin of this reduced activity, thus enabling site managers to continuously improve the progress schedule of the site in terms of material (with better management of equipment for example) and / or human and / or logistical / organizational aspects.

[0058] The diagnostic method 1 is applicable to all types of crane. In the context of the description, with reference to [Fig.2], it is considered that the crane 2 is a tower crane, more precisely a crane with a distributing jib.

[0059] With reference to [Fig.l] and [Fig.2], the diagnostic method 1 begins with a step E1 of detection and collection of data which comprises: - a detection El 1 and a collection El3 of crane data DI which include equipment data DU from equipment 3, 31 of crane 2, or else sensor data D12 from sensors 5 installed on the latter; - a detection E12 and a collection E14 of environmental data D2 representative of the environment of the construction site in which the crane is operating. The environmental data may be provided by a sensor 5 fitted to the crane; an external system 7 which is external to the crane 2 but installed on the construction site; or a system / entity external to the construction site (not illustrated in [Fig.2]). As will be specified later, the environmental data may include climatic data providing information on the meteorological conditions to which the construction site is subjected. In this case, the external system / entity may correspond to a meteorological website providing climatic data relating to the country, and / or the region, and / or the city where the construction site is located.

[0060] The crane data D1, DI 1, D12 may include, but are not limited to / exhaustively: - data relating to the location of structural, functional and crane 2 ballast; - positioning data for the raising or lowering of the pulley block when lifting a load; - data representative of the presence or activity of a crane operator in the crane; - maneuver counting data; - data representative of a stop controlled by an anti-collision system. These data are sources of information for construction sites comprising several cranes, in particular if the circular working areas of the 2 cranes overlap because they make it possible to determine the frequency of risks of collision between the booms of the 2 cranes, and the frequency of stops of the 2 cranes; - load lifting cycle counting data; - data representative of pause times between two maneuvers; - data representative of types of maneuver; - data relating to the type and / or model of crane 2 (for example, if crane 2 is a tower crane: self-erecting crane, self-erecting crane, distributing jib crane, luffing jib crane, etc.). The type of crane is an important piece of data because it is adapted to the environmental context of the construction site (rapidly erected cranes are used for individual constructions, while self-erecting cranes or self-erecting cranes are used for the construction of buildings, etc.). The usage guidelines are therefore different from one type of crane to another; - etc.

[0061] The crane data D1, D11, D12 also include work data DW which are representative of work carried out by the crane 2 such as, but not limited to / exhaustively, load / overload data when lifting a load; speed data of the structural element being operated (for example, a rotation speed of the boom).

[0062] The environmental data D2 includes at least: data relating to the location of the construction site; and climatic data such as temperature data, wind speed data and hygrometric data.

[0063] In one embodiment of the invention, the collection E13, E14 of the crane data D1, DU, D12, DW and the environmental data D2 corresponds first of all to a recovery of the latter by the control-command system of the crane 2, which is therefore in communication with the equipment 3, 31 and sensors 5 of the crane 2, the external system 7. Once it has recovered them, the control-command system transmits the crane data D1, D11, D12, DW and the environmental data D2 to a computer infrastructure responsible for processing them for determining the periods of reduced activity LAP of the crane 2 among a plurality of periods of activity and the causes explaining them.

[0064] In a second embodiment of the invention, which corresponds to the embodiment described, the collection E13, E14 of the crane data D1, DU, D12, DW and the environmental data D2 corresponds to a direct transmission of said data D1, DU, D12, DW, D2 by the equipment 3, 31, the sensors 5, and the external system 7 to the IT infrastructure.

[0065] In the embodiment presented, the IT infrastructure comprises: - a remote analysis system 6 in communication designed to receive all the data D1, D11, D12, DW, D2 and process them during processing E4, E5 to determine the periods of reduced LAP activity of the crane 2 and their origin(s). The remote analysis system may, for example, correspond to a computer (desktop; or portable; or embedded / fanless type) which will be used by an operator (such as an analyst, the site manager, etc.) - a remote database 4 in communication with the remote analysis system 6, and which is used for storing crane data D1, D11, D12, DW and environmental data D2.

[0066] In an alternative embodiment of the invention, the remote database 4 is included in the remote analysis system 6.

[0067] In different embodiments of the invention, depending on the duration considered for the activity period, and the analysis needs of the site managers, the data D1, D11, D12, DW, D2 can be transmitted to the remote analysis system continuously or intermittently (transmission at the end of the day for example).

[0068] As the data D1, D11, D12, DW, D2 may come from different types of equipment 3, 31; and / or sensors 5 of the crane 2; and / or external systems 7, they may have different formats. This is why the remote analysis system 6 proceeds, following the detection and collection step E1, to a data formatting step E2 during which cleaning and structuring of all the data D1, DU, D12, DW, D2 are implemented.

[0069] The data formatting step E2 aims to make the data D1, D11, D12, DW, D2 more easily interpretable by the remote analysis system 6 when it subsequently performs the processing operations E4, E5 on them in order to determine the periods of decrease in LAP activity and their origins.

[0070] Indeed, the determination and analysis of the causes of a period of AP activity as being or not being a period of decrease in LAP activity are carried out a posteriori, once the period of activity has temporally ended. The determination and analysis may also not be carried out immediately at the end of the period of AP activity, but much later, after at least one other period of AP activity has taken place. For example, if this period of AP activity corresponds to the second week of a given month and a site manager wishes to determine whether crane 2 has experienced a drop in activity over all the weeks making up the month in question, then the data D1, DU, D12, DW, D2 relating to this second week will only be processed at the very least at the end of the month.

[0071] Consequently, a historicization E3 is implemented following the data formatting step E2, during which the data D1, D11, D12, DW, D2 will be historicized by AP activity period in the remote database 4 once the remote analysis system 6 has imported them there. Thus, if, for example, five specific AP activity periods among a plurality of activity periods must be analyzed, the remote analysis system 6 will only export the data D1, DU, D12, DW, D2 relating to said five AP activity periods.

[0072] Following the historicization E3 of the data D1, D11, D12, DW, D2 of at least one period of activity AP, the processing steps E4, E5 are implemented at a time t for its analysis.

[0073] In one embodiment of the invention, all or part of the processing E4, E5 is carried out directly by the remote analysis system 6 directly in the remote database 4.

[0074] In the embodiment presented, the processing E4, E5 is carried out only at the level of the remote analysis system 6, with export of all the D1, DU, D12, DW, D2 that it needs to carry out the analysis of the period(s) of AP activity considered.

[0075] Subsequently, the treatments E4, E5 are designated under the names of first treatment E4 and second treatment E5.

[0076] During the first processing E4, a working time Ho of the crane 2 is calculated for at least one period of activity AP studied from its associated working data DW, i.e. the time during which the crane was active / working during this period.

[0077] This working time Ho is then compared to an activity threshold. If the working time Ho of the AP activity period studied is lower than this activity threshold, then it is considered to be a decrease in the LAP activity period.

[0078] In a first case, the activity threshold corresponds to a fixed value representative of an average time objectified by the construction site manager; meaning that a period of AP activity is determined as being or not being a period of reduced LAP activity on the sole basis of its associated DW work data.

[0079] In a second case, the activity threshold corresponds to a variable value, for example an average value of the working times Ho of several periods of AP activity including: the period of AP activity studied / of interest, and periods of activity which are prior and / or subsequent to it. Consequently, in this second case, the first E4 processing relies on the analysis / processing of DW work data from multiple AP activity periods to determine whether or not the AP activity period of interest is a period of LAP activity decline.

[0080] The second processing E5 is implemented in the case where a period of AP activity studied (among or not a plurality of periods of AP activity currently being processed) is identified as a period of decrease in LAP activity. The second processing E5 aims to contextualize the cause(s) explaining this period of decrease in LAP activity. For this, mathematical algorithms are applied to the crane data D1, DU, D12, DW and to the environmental data D2, which compare them to decision criteria perceptible / understandable by the operator of the remote analysis system analyzing the period of activity.

[0081] The decision criteria leading to the identification of a cause of a drop in activity can be defined solely from an interpretation of the data D1, D11, D12, DW, D2 from the period of drop in LAP activity studied; and / or from the combined interpretation of several data sets D1, D11, D12, DW, D2 from the period of drop in LAP activity studied on the one hand, and from several other periods of AP activity on the other hand (whether or not they are periods of drop in LAP activity).

[0082] Based on the comparison results, the remote analysis system 6 determines that the cause of the decrease in crane activity during the period of decreased activity corresponds to an anomaly type A1, A2, A3, A4.

[0083] In the embodiment of the diagnostic method 1 presented, four types of anomaly A1, A2, A3, A4 are defined: - an internal anomaly Al, and identified from crane data D1, D11, D12, DW including those listed previously; - an anomaly in the use of crane 2, A2, also identified from the data of craneDl,Dll,D12, DW; - an A3 climatic anomaly reflecting an extreme climatic condition associated with bad weather, and identified from climatic data included in the D2 environmental data; and - an organizational anomaly A4; and identified from crane data D1, D11, D12, DW.

[0084] In each type of anomaly A1, A2, A3, A4 several sub-categories (or natures) of anomaly A11, A12, A13, A21, A22, A31, A32, A41, A42 are also defined.

[0085] An internal anomaly Al may correspond to a hardware fault Al 1, a software fault A12 or a communication fault A13 of a faulty equipment 31 of the crane. This or these fault(s) may result from a malfunction or a breakdown of equipment or a system due, for example, to lack of maintenance, repairs not carried out, or wear and tear (the equipment or system reaching the end of its life).

[0086] The usage anomaly A2 may correspond to: - an assembly anomaly A21 reflecting incorrect installation of crane 2, and / or adjustment of its structural, functional or ballast elements. For example, in order to meet specific needs of the construction site, specific adjustments must be made to the elements and equipment of crane 2 to perform additional functions. However, these adjustments may not have been anticipated before the start of the construction site, meaning that delays will occur at given times during the life cycle of the site to carry out these adjustments, without which it will not be able to progress. Delays may also be caused by an unforeseen change in the initial configuration of the construction site, with the emergence of new, previously unknown needs - to a piloting anomaly A22, i.e. inappropriate piloting of the crane by the crane operator (lifting a load too quickly, rotating the boom too quickly, etc.).

[0087] The climatic anomaly A3 includes different subcategories of anomaly A31, A32 relating for example to the nature of the extreme climatic condition: strong gusts of wind A31; heavy rain A32; very high or very low temperatures.

[0088] Finally, the organizational anomaly A4 may correspond to an activity anomaly Al of the crane, i.e. the crane is inactive most of the time during the activity period AP with very little load to lift or move (or in the worst case no load); or a management anomaly A2 due to an organizational lack in the management of the construction site, logistical delays, etc.

[0089] The decision criteria allowing the diagnostic method 1 to determine which type(s) of anomaly A1, A2, A3, A4 or subcategory(ies) of anomaly A11, A12, A13, A21, A22, A31, A32, A41, A42 is / are at the origin of a period of decrease in LAP activity are not limited in number.

[0090] According to different embodiments of the invention, one or more decision criteria is / are defined in the diagnostic method 1, and integrated into the remote analysis system 6, for the determination of the same type of anomaly A1, A2, A3, A4 or of the same sub-category of anomaly A11, A12, A13, A21, A22, A31, A32, A41, A42.

[0091] Depending on his analysis needs, the user selects in the remote analysis system 6, during the second processing E5, the criterion or criteria which appear to him to be the most relevant.

[0092] In one embodiment of the invention, an option is available in the remote analysis system 6 so that the user can define new criteria for decision relating to a type of anomaly A1, A2, A3, A4 and / or to a subcategory of anomaly A11, A12, A13, A21, A22, A31, A32, A41, A42, these new decision criteria being added to those already available in the remote analysis system (and which were created by the designers of the invention), and then being used by the diagnostic method 1 during its execution.

[0093] In a second embodiment of the invention, the diagnostic method 1 can be enriched and improved by providing / defining new anomalies, i.e. anomalies that have never been encountered on the construction site. The definition of the new anomaly consists at least in providing at least one decision criterion that will be applied by the diagnostic method 1 during the second processing E5 of the crane data D1, DU, D12, DW and the environmental data D2 so that it identifies that the drop in activity is due to this new type of anomaly.

[0094] Two application examples of the diagnostic method 1 for the analysis of AP activity period analysis are presented and illustrated [Fig.3] and [Fig.4]. In these two examples, the activity of crane 2 is analyzed for AP activity periods corresponding to weeks extending from January 2019 and July 2020.

[0095] For both examples, during the first processing E4, the working time Ho during which the crane 2 was active / working is calculated for each week / activity period AP, from the working data DW acquired by the remote analysis system 6 of the IT infrastructure during all of said weeks. The working time Ho is calculated and given in hours.

[0096] With reference to [Fig.3], in the context of the first application example, for each week / period of AP activity, the activity threshold is defined as being equal to the average value between: - a first value equal to 0.7 times the average value of the working time Ho of the three weeks preceding a week considered among the several weeks, from which ten hours are subtracted; and - a second value equal to 0.7 times the working time Ho of the week following the week considered among the several weeks, from which ten hours are subtracted

[0097] If the working time Ho of a week among the several weeks of the activity period is greater than or equal to the activity threshold, then no drop in activity is observed for said week. If, conversely, the working time Ho of the week considered is less than the activity threshold, then a drop in activity is observed for it, and the week is considered to be suspect.

[0098] Thus, a period of decreased LAP activity corresponds either to one suspect week or to several consecutive suspect weeks.

[0099] In order to determine the causes of the drops in activity in the suspect weeks detected during the first E4 treatment, the working time Ho for each week / period AP activity is compared with the crane data Dl, DU, D12 associated with these suspect weeks during the second E5 treatment, which were also recorded for each week / period of AP activity.

[0100] In the context of this first application example, the crane data D1, D11, D12 considered corresponds to the number of lifting faults NG due to a malfunction of faulty equipment 31 contributing to the lifting of the load by the crane 2, which malfunction relates to an internal anomaly Al, more precisely to a material fault Ail.

[0101] In order to determine whether the malfunction of the faulty equipment 31 is the cause of the periods of reduced activity LAP, an average value, called the average value of lifting fault NG, is first calculated for the at least one suspect week included in a period of reduced activity, between the number of lifting faults NG recorded for the at least one suspect week and that of the week preceding the at least one suspect week.

[0102] A median value of the number of NG lifting faults is then calculated over all AP activity periods, i.e. between January 2019 and July 2020.

[0103] A decision criterion is then applied such that the malfunction of the faulty equipment 31 is one of the causes at the origin of a period of decrease in LAP activity of the crane when: the average value of lifting fault NG of said period of decrease in LAP activity is greater than the median value of the number of lifting faults NG.

[0104] After applying this decision criterion, the diagnostic method 1 concludes that the malfunction of the faulty equipment 31 is partly the cause of the drop in activity observed for a suspect week between October 2019 and January 2020 (as a reminder, a drop in activity during a period of reduced LAP activity may have a single or multiple origins).

[0105] With reference to [Fig.4], in the context of the second application example, the evolution of the working time of the crane over the activity period is compared to a fixed activity threshold which is equal to 27 hours.

[0106] A period of decreased LAP activity is detected when the working time Ho becomes lower than the fixed activity threshold.

[0107] Four periods of decline in activity are then identified: - a first period of decline in LAP activity between April 2019 and July 2019, - a second period of decline in LAP activity between July 2019 and October 2019, - a third period of decline in LAP activity between October 2019 and January 2020, and - a fourth period of decline in LAP activity between January 2020 and April 2020.

[0108] The crane data Dl, Dl 1, D12 considered during the second processing E5 for this second application example again corresponds to the number of NG lifting faults due to a malfunction of faulty equipment 31.

[0109] In order to determine whether the malfunction of the faulty equipment 31 is the cause of the drops in activity of the four activity periods, the following criterion is applied: if the number of NG lifting faults is non-zero during a period of drop in LAP activity, then the malfunction of the equipment partly explains the drop in activity observed during this period of drop in LAP activity.

[0110] In this second application example, the diagnostic method 1 concludes that the malfunction of the faulty equipment 31 is a cause at the origin of the drop in activity of the first and third periods of drop in LAP activity. Indeed, two (respectively six) NG lifting faults are concomitant with the first (respectively the third) period of drop in LAP activity.

[0111] On the other hand, no NG lifting fault was observed during the second and fourth periods of reduced LAP activity, meaning that the malfunction of the faulty equipment 31 is not a cause at the origin of these. Thus, the diagnostic method 1 must relate other crane data D1, DI 1, D12 and / or the environmental data D2 with the working time Ho of the second and fourth periods of reduced LAP activity to determine the causes explaining them.

[0112] In one embodiment of the invention, the operator can inform / define by means of the remote analysis system 6 specific AP activity periods for which drops in activity are expected and known to the managers, for example school holiday periods. If following the application of the first processing E4 a period of LAP activity drop is identified which coincides with a specific AP activity period, then the diagnostic method 1 does not consider it as a period of LAP activity drop but as a period of normal AP activity.

[0113] In a first embodiment of the invention, the diagnostic method 1 implements, in parallel with the processing E4, E5 of the data D1, DU, D12, DW for each period of activity AP, a display of a progress of said processing E4, E5 on a screen integrated into the remote analysis system 6 or connected to it.

[0114] In a second embodiment of the invention, new anomaly subcategories can be added to the predefined anomalies A1, A2, A3, A4 and to any new anomaly. The creation of a new anomaly subcategory also requires defining at least one decision criterion must be defined so that it is identified by the diagnostic method during the second processing E5.

[0115] In a third embodiment of the invention, it is conceivable that the definition of a new anomaly requires an evolution of the format used for the cleaning and structuring of the data D1, D11, D12, DW, D2, in the case where the new anomaly is determined from data having a non-ac format actually taken into account by the diagnostic method 1 during the data formatting step E2.

[0116] In a fourth embodiment of the invention, the addition of a new anomaly or a new subcategory of anomaly is carried out by the operator himself from the remote analysis system 6.

[0117] In a fifth embodiment of the invention, the addition of a new anomaly or a new subcategory of anomaly is carried out by the designers of the diagnostic method 1 on the basis of customer feedback; the additions of anomaly or subcategory of anomaly then being part of an agile development approach for continuous improvement of the diagnostic method 1. It is conceivable that the new definitions of anomaly or subcategories of anomaly are contained in updates of the diagnostic method 1 available for downloading and which can be downloaded from the remote analysis system 6.

[0118] Once the second processing E5 is completed, the remote analysis system 6 implements a generation E6 and / or a display E7 of an analysis report relating to the period(s) of AP activity considered. This analysis report indicates in particular the periods of decrease in LAP activity identified as well as the anomalies A1, A2, A3, A4 and the anomaly subcategories A11, A12, A13, A21, A22, A31, A32, A41, A42 explaining them.

[0119] In a first embodiment, the analysis report may take the form of a file edited in a given format and comprising a complete detail of the processing and calculations carried out on the data D1, DU, D12, DW, D2 associated with one or more periods of AP activity, and which led the diagnostic method 1 to identify among them one or more periods of decrease in LAP activity and the anomalies A1, A2, A3, A4 and sub-categories of anomaly A11, A12, A13, A21, A22, A31, A32, A41, A42 explaining them.

[0120] In a second embodiment, the analysis report may be presented in the form of a visual representation, such as a graph 100 for example, so as to be quickly and easily interpretable and understandable by the operator of the remote system 6, especially if he is not an expert in data analysis. It is conceivable, for example, that one or more graphs 100 associated respectively with one or more periods of decrease in LAP activity identified among several periods of AP activity be displayed on the screen, with for each graph 100 a visual representation of the at least one type of anomaly A1, A2, A3, A4 and the at least one subcategory of anomaly A11, A12, A13, A21, A22, A31, A32, A41, A42 identified, for example: a color, text giving the name of the anomaly and associated with a color, etc.

[0121] For example, with reference to [Fig.4] a hierarchical graph 100 called a sunburst graph is illustrated for a period of decreased LAP activity, with: the inner ring IR segmented into several pieces of inner ring IRP corresponding to the anomalies A1, A2, A3, A4; and the outer ring OR segmented into several pieces of outer ring ORP corresponding to the anomaly subcategories A11, A12, A13, A21, A22, A31, A32, A41, A42. The surfaces of each IRP inner ring piece (respectively of each ORP outer ring piece) are representative of the occurrence of anomaly Al, A2, A3, A4 (respectively of anomaly subcategory Ail, A12, A13, A21, A22, A31, A32, A41, A42) during the period of decreased LAP activity.

[0122] Thus, if during a period of decreased LAP activity an anomaly Al, A2, A3, A4 or an anomaly subcategory A11, A12, A13, A21, A22, A31, A32, A41, A42 has not been identified, it will not appear on the graph and the surfaces of the inner ring pieces IRP and the outer ring pieces ORP will adapt accordingly (since it reflects an occurrence of anomaly Al, A2, A3, A4 or anomaly subcategory A11, A12, A13, A21, A22, A31, A32, A41, A42).

[0123] In the example given [Fig.4]-b, all anomalies A1, A2, A3, A4 and anomaly subcategories A11, A12, A13, A21, A22, A31, A32, A41, A42 are identified for a first period of decline in LAP activity, with a large part of the decline in productivity of crane 2 being able to be explained by a lack of activity of the latter or organizational shortcomings concerning the construction site (nearly 50% of the decline in crane activity is due to an organizational anomaly A4).

[0124] In the example given [Fig.4]-c, internal anomalies A1, usage anomalies A2 and organizational anomalies A4 are identified for a second period of decline in LAP activity. Compared to the first period of decline in LAP activity, there was no climatic anomaly A3 during this period. Also, the internal anomalies A3 are only due to software defects A12 and communication defects A13. It emerges from the analysis of this second period of activity that the decline in activity of crane 2 is mainly due to usage anomalies A2 (in particular piloting anomalies A22) and organizational anomalies A4.

[0125] In one embodiment of the invention, it is conceivable that the operator can interact with the graph 100 displayed on the screen. For example, when he clicks with a desktop mouse on a surface of a piece of inner TRP or outer ORP ring, a new window is displayed on the screen and contains all the calculations and processing relating to the anomaly A1, A2, A3, A4 or the anomaly subcategory A11, A12, A13, A21, A22, A31, A32, A41, A42 associated with this surface.

Claims

Claims

1. A method (1) for diagnosing an activity of a crane (2) for detecting and classifying a period of reduced activity (LAP) of said crane (2) in a construction site among several periods of activity (AP), said diagnostic method (1) implementing at least the following steps: - detection (Eli) of crane data (Dl, DU, D12, DW) from equipment (3, 31) of the crane, and comprising at least work data (DW) representative of work of the crane (2) implementing at least one maneuver of at least one structural element of the crane (2); - detection (E12) of environmental data (D2) representative of an environment of the construction site; - historization (E3) by period of activity (AP) of the crane data (Dl, DU, D12, DW) and the environmental data (D2) in a remote database (4);- for each activity period (AP), processing (E4) of the work data (DW) to calculate a work time (Ho) of the crane (2) during the activity period (AP), and comparing said work time (Ho) with at least one activity threshold to determine whether said activity period (AP) is a period of reduced activity (LAP) or not; - for each period of reduced activity (LAP), processing (E5) of the crane data (Dl, DU, D12, DW) and of the environmental data (D2) associated at least with said period of reduced activity (LAP) to identify at least one anomaly (A1, A2, A3, A4) of the construction site or of the crane (2), which anomaly (A1, A2, A3, A4) is associated with said period of reduced activity (LAP).;

2. Diagnostic method (1) according to claim 1, wherein the at least one anomaly (A1, A2, A3, A4) comprises at least one internal anomaly (A1) of the crane (2) reflecting a technical failure of the crane (2) and identified from the crane data (D1, D11, D12, DW).

3. Diagnostic method (1) according to claim 2, wherein the at least one internal anomaly (A1) comprises at least one hardware (A11), software (A12) or communication (A13) fault of one of the equipment (31) called faulty equipment (31), identified from the crane data (D1, DU) from said faulty equipment (31).

4. A diagnostic method (1) according to any one of claims previous, in which the at least one anomaly (A1, A2, A3, A4) comprises at least one usage anomaly (A2) reflecting non-compliant use of the crane and identified from the crane data (D1, D11, D12, DW).

5. Diagnostic method (1) according to claim 4, in which the at least one usage anomaly (A2) comprises at least one assembly anomaly (A21) reflecting an assembly and / or adjustment of equipment that is not compliant or not adapted to the construction site, and identified from sensor data (D12) chosen from the crane data (D1, DI 1, D12, DW) and originating from at least one sensor (5) of the crane (2).

6. Diagnostic method (1) according to claim 4 or 5, in which the at least one usage anomaly (A2) comprises at least one piloting anomaly (A22) reflecting non-compliant piloting of the crane (2) by a crane operator during maneuvers, and identified from the work data (DW), such as for example speed data of at least one structural element of the crane (2) or overload data.

7. Diagnostic method (1) according to any one of the preceding claims, in which the at least one anomaly (A1, A2, A3, A4) comprises at least one climatic anomaly (A3) reflecting an extreme climatic condition and identified from climatic data chosen from the environmental data (D2).

8. A diagnostic method (1) according to claim 7, wherein the climate data comprises at least one of the following data: temperature data, wind speed data and hygrometric data.

9. Diagnostic method (1) according to any one of the preceding claims, wherein the at least one anomaly (A1, A2, A3, A4) comprises at least one organizational anomaly (A4) reflecting low profitability of the use of the crane and identified from the crane data (D1, DU, D12, DW).

10. Diagnostic method (1) according to claim 9, in which the at least one organizational anomaly (A4) is identified from at least one of the following data among the crane data (D1, DU, D12, DW): data representative of a presence or activity of the crane operator in the crane (2), maneuver counting data, data representative of a stop controlled by an anti-collision system, load lifting cycle counting data, data representative of pause time between two maneuvers, data representative of types of maneuver, data representative of a type of crane (2).

11. Diagnostic method (1) according to any one of the preceding claims, wherein a remote analysis system (6), in communication with or comprising the remote database (4), implements the processing (E4, E5) of the work data (DW), the crane data (D1, DU, D12, DW) and the environmental data (D2) to determine whether each activity period (AP) is a period of reduced activity (LAP) or not and to associate with each period of reduced activity (LAP) the at least one corresponding anomaly (A1, A2, A3, A4).

12. Diagnostic method (1) according to claim 11, wherein the remote analysis system (6) structures the crane data (D1, DU, D12, DW) and the environmental data (D2) in the same predefined format.

13. Diagnostic method (1) according to any one of the preceding claims, in which the diagnostic method (1) implements, after the processing (E4, E5) of the crane data (D1, DU, D12, DW) and the environmental data (D2) of each period of reduced activity (AP), a generation (E6) and / or a display (E7) of an analysis report comprising, for the or each period of reduced activity (LAP), information specific to the at least one anomaly (A1, A2, A3, A4) identified.

14. Diagnostic method (1) according to any one of the preceding claims, in which, for each activity period (AP), the activity threshold for said activity period (AP) corresponds to an average value of the working times (Ho) of several activity periods (AP) prior and / or subsequent to said activity period (AP).

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