Method for diagnosing crane activity to determine abnormalities resulting from a drop in activity

Description

[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 specifically to a diagnostic process in which data relating to the crane and its environment are analyzed, making it possible to determine over time periods when the productivity / activity of the crane may 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. [State of the art]

[0004] As is well known, understanding and analyzing the causes of schedule delays and reduced productivity on a construction site can be particularly complex due to the many human and material actors 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 weather condition can result in the crane being shut down, regardless of its proper functioning. There are also other causes of a decrease in activity such as, but not limited to: a breakdown due to a malfunction / shutdown 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, documents US 2012 / 0158279 and US 2018 / 0018641 propose to exploit the data provided by the equipment of a machine, such as a crane, with the aim of providing performance indicators on it or ensuring 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).

[0008] Documents CN107025521 and DE102015006992 propose to use equivalent data with the aim of respectively providing an optimized work plan for the crane, and optimizing the capacities of the crane according to the intensities of its loads to avoid any overload situation.

[0009] Document US 2009 / 055039 A1 discloses a method for diagnosing crane activity 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 an environment of the construction site; logging by activity period of crane data and environmental data in a remote database.

[0010] The state of the art can also be illustrated by the teachings of document CN108190745 which discloses a data collection system receiving data from a plurality of crane equipment in order to determine, when analyzed, malfunctions or breakdowns of said equipment.

[0011] 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]

[0012] 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 an environment of the construction site, and comprising at least climatic data; historization by period of activity of the crane data and the 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 the crane data and the 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.

[0013] 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 structural elements is maneuvered (for example the block which is raised when a load is lifted, or the boom which rotates to move the load following its lifting), and environmental data representative of the environment and the location of the construction site in which the crane is operating, which data are collected during one or more given activity periods (an activity period can 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, the diagnostic process is capable of: determining whether or not a drop in crane activity has occurred for at least one activity period considered.To do this, the process calculates, from data representative of the crane's work, its activity time during the activity period, i.e. the total duration during this activity period when 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, thus allowing the 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.

[0014] 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.

[0015] 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 equipment called faulty equipment, identified from crane data from said faulty equipment.

[0016] In other words, the diagnostic process is designed to determine whether the anomaly responsible for a drop in crane activity 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 process from data provided, for example, by the equipment / components of the crane themselves, or by the crane's breakdown manager.

[0017] 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 a lack of maintenance, a repair not carried out, or wear in the context of consumable equipment.

[0018] 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.

[0019] 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.

[0020] 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 work data, such as for example speed data of at least one structural element of the crane or overload data.

[0021] In other words, the diagnostic process 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.

[0022] 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.

[0023] This type of anomaly is determined by the diagnostic process from crane data provided for example by equipment / components or crane sensors, 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 of the raising or lowering of the block, etc.

[0024] 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.

[0025] 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.

[0026] According to one characteristic of the invention, the climatic data comprises at least one of the following data: temperature data, wind speed data and hygrometric data.

[0027] In other words, the diagnostic process is designed to determine whether the anomaly responsible for a decrease in crane activity during a given period of activity is caused by extreme climatic conditions, generally associated with bad weather, such as: very high or very low outdoor temperatures; very heavy rain; very strong gusts of wind. This type of anomaly is determined by the diagnostic process from environmental data such as temperature data, wind speed data and hygrometric data, which are provided by suitable measuring devices (such as outdoor temperature sensors, anemometers for measuring wind speed, etc.).

[0028] 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, particularly if these climatic anomalies are recurrent: reduction of the activity of site workers during periods of high temperatures to avoid them having an impact on their condition / health, weathervaning of the cranes if there is a risk of strong gusts of wind, etc.

[0029] 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.

[0030] 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 controlled by an anti-collision system, load lifting cycle counting data, data representative of pause time between two maneuvers, data representative of maneuver types, data representative of a type of crane.

[0031] In other words, the diagnostic process 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 reflected, 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 absence of a crane operator in the crane means that it is inactive, or if he is present and performs few maneuvers, the break time between each maneuver is significant), data on counting load lifting cycles, 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 are hindering each other when carrying out their respective tasks.

[0032] 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 among which one of them 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.

[0033] Similar to the first processing carried out to determine whether or not a period of activity corresponds to a period of reduced activity, the processing implemented to identify the type(s) of anomaly (internal anomaly, usage anomaly, climatic anomaly, organizational anomaly) explaining the reduced activity consists 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 comprise the type (or category) of anomaly.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.

[0034] According to a feature 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.

[0035] 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.

[0036] According to one embodiment of the invention, the remote database is included in the remote analysis system.

[0037] 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.

[0038] 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.

[0039] According to a feature of the invention, the remote analysis system structures the crane data and the environmental data in the same predefined format.

[0040] Since work data, crane data and environmental data come from different equipment, they can be in different formats. For each activity period, before the data associated with each activity period is imported into the remote database, they are, once collected, transmitted to the remote analysis system which will clean and structure them according to a predefined format; in order to be more easily interpretable by the remote analysis system when it subsequently performs processing on the data to determine periods of reduced activity and their origins.

[0041] 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.

[0042] 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.

[0043] This generation and / or 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 performed during the processing of the crane data and environmental data that led to the identification of the at least one anomaly.

[0044] In other words, in order for the results of the processing to be easily interpretable and understandable for 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 behind it, 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 that can be consulted later, and / or display this analysis report on a screen. In different embodiments of the invention, the analysis report can comprise: a visual representation of the at least one type / category of anomaly identified for each of the periods of activity 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 conceivable that each subcategory of a type / category of anomaly also has its own visual representation to improve the 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 that 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 which specifically led the diagnostic process to identify this subcategory.

[0045] According to a feature 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.

[0046] Advantageously, the diagnostic process can be enriched and improved by providing information on / 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 information on at least one decision criterion that will be applied by the diagnostic process 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 process 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 process can identify 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 devices 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.

[0047] In a first variant embodiment of the invention, the anomaly definition additions are made from the remote analysis system by the users themselves.

[0048] In a second embodiment of the invention, the additions to the anomaly definition are made by the designers of the diagnostic method on the basis of customer feedback; the additions to the anomaly definition 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 to the diagnostic method available for downloading from the remote analysis system.

[0049] 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.

[0050] In one embodiment of the invention, for each activity period, the activity threshold for said activity period corresponds to an average value of the working times of several activity periods prior and / or subsequent to said activity period.

[0051] Thus, the activity times of previous and / or subsequent periods are taken into consideration in order to set the activity threshold for the period analyzed.

[0052] Advantageously, the environmental data includes, in addition to climatic data, topographic data representative of the surrounding topography of the site.

[0053] Indeed, such topographical data makes it possible to refine the local context of the site, to better understand the sources of anomalies. [Brief description of the figures]

[0054] 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: [ Fig 1 ] is a flowchart illustrating a diagnostic method of the invention; [ Fig 2 ] is a schematic view of an implementation of the diagnostic method in the context of determining one or more periods of reduced activity among several periods of activity of a distributing jib crane, by means of the equipment and sensors of the crane, as well as an external system, transmitting crane data and environmental data to a remote analysis system which will process them with a view to said determination of the periods of reduced activity; [ Fig 3] illustrates a first example of application of the diagnostic method to identify and contextualize 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; [ Fig 4] illustrates a second example of application of the diagnostic method to identify and contextualize 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; [ Fig 5] is an illustration of an example of a 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 method; with here two examples of suspect weeks such that for one of them all the anomalies and anomaly subcategories defined were observed (b), and for the other some of the anomalies and anomaly subcategories among all the anomalies and anomaly subcategories defined (c). [Detailed description of one or more embodiments of the invention]

[0055] In reference to the Figure 1 to Figure 4 , the invention relates to a method of diagnosing 1 an activity of a crane 2 in front, from an analysis of a plurality of data in relation to the crane 2 and its environment: determine whether or not a drop in crane 2 activity occurred during a given AP activity period, an AP activity period being for example a day, a week, a month. A AP activity period during which a drop in activity was observed is called a LAP activity drop period; in the case where a period of drop in activity was detected, determine and contextualize the causes, subsequently designated as anomalies A1, A2, A3, A4 at the origin of this drop in activity, thus 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.

[0056] Diagnostic method 1 is applicable to all types of crane. In the context of the description, with reference to the Figure 2, it is considered that crane 2 is a tower crane, more precisely a distributing jib crane.

[0057] In reference to the Figure 1 and Figure 2 , the diagnostic method 1 begins with a step of detection and collection E1 of the data which includes: a detection E11 and a collection E13 of crane data D1 which include equipment data D11 from equipment 3, 31 of the crane 2, or 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 equipping 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 on the Figure 2). As will be clarified later, environmental data may include climate data that provides information on the weather conditions experienced by the construction site. In this case, the external system / entity may be a weather website that provides climate data for the country, and / or region, and / or city where the construction site is located.

[0058] Crane data D1, D11, D12 may include, but are not limited to: data relating to the location of the structural, functional and ballast elements of the crane 2; positioning data for the raising or lowering of the 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 related to the type and / or model of the 2 crane (for example, if the 2 crane is a tower crane: element-mounted 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 element-mounted cranes or self-erecting cranes for the construction of buildings, etc.).The usage guidelines therefore differ from one type of crane to another; etc.

[0059] The crane data D1, D11, D12 also includes work data DW which is representative of work carried out by the crane 2 such as, but not limited to, load / overload data when lifting a load; speed data of the structural element being operated (e.g., a rotation speed of the boom).

[0060] D2 environmental data includes at least climate data such as temperature data, wind speed data and humidity data.

[0061] D2 environmental data may also include topographic data representative of the surrounding topography of the construction site, such as the presence of the construction site in a valley, the presence of neighboring buildings, etc.

[0062] In one embodiment of the invention, the collection E13, E14 of the crane data D1, D11, 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.

[0063] In a second embodiment of the invention, which corresponds to the embodiment described, the collection E13, E14 of the crane data D1, D11, D12, DW and the environmental data D2 corresponds to a direct transmission of said data D1, D11, D12, DW, D2 by the equipment 3, 31, the sensors 5, and the external system 7 to the IT infrastructure.

[0064] In the presented embodiment, the IT infrastructure comprises: a remote analysis system 6 in communication configured to receive all the data D1, D11, D12, DW, D2 and process them during processing E4, E5 for determining 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...) a remote database 4 in communication with the remote analysis system 6, and which is used for storing the crane data D1, D11, D12, DW and the environmental data D2.

[0065] In an alternative embodiment of the invention, the remote database 4 is included in the remote analysis system 6.

[0066] 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).

[0067] 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, D11, D12, DW, D2 are implemented.

[0068] 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 E4, E5 on them in order to determine the periods of decline in LAP activity and their origins.

[0069] Indeed, the determination and analysis of the causes of a period of AP activity as being or not a period of reduced 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 reduction in activity over all the weeks making up the month in question, then the data D1, D11, D12, DW, D2 relating to this second week will only be processed at least at the end of the month.

[0070] Therefore, 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, D11, D12, DW, D2 relating to said five AP activity periods.

[0071] Following the historicization E3 of the data D1, D11, D12, DW, D2 of at least one period of AP activity, the processing steps E4, E5 are implemented at a time t for its analysis.

[0072] 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.

[0073] 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, D11, D12, DW, D2 which it needs to carry out the analysis of the period(s) of AP activity considered.

[0074] Subsequently, treatments E4, E5 are referred to as first treatment E4 and second treatment E5.

[0075] During the first processing E4, a working time Ho of 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.

[0076] 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.

[0077] In the 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 a period of reduced LAP activity on the sole basis of its associated DW work data.

[0078] 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 processing E4 is based on the analysis / processing of the work data DW from several periods of AP activity to determine whether or not the period of AP activity of interest is a period of decline in LAP activity.

[0079] 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 LAP activity decline. The second processing E5 aims to contextualize the cause(s) explaining this period of LAP activity decline. For this, mathematical algorithms are applied to the crane data D1, D11, 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 activity period.

[0080] 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 from 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).

[0081] Based on the comparison results, the remote analysis system 6 determines that the cause of the crane's reduced activity during the reduced activity period corresponds to an anomaly type A1, A2, A3, A4.

[0082] In the embodiment of the diagnostic method 1 presented, four types of anomaly A1, A2, A3, A4 are defined: an internal anomaly A1, and identified from crane data D1, D11, D12, DW including those listed previously; a usage anomaly A2 of crane 2, also identified from crane data D1, D11, D12, DW; a climatic anomaly A3 reflecting an extreme climatic condition associated with bad weather, and identified from climatic data included in environmental data D2; and an organizational anomaly A4; and identified from crane data D1, D11, D12, DW.

[0083] 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.

[0084] An internal anomaly A1 may correspond to a hardware fault A11, a software fault A12 or a communication fault A13 of a faulty piece of equipment 31 of the crane. This or these fault(s) may result from a malfunction or breakdown of equipment or a system due, for example, to its lack of maintenance, an unperformed repair, or wear (the equipment or system reaching the end of its life).

[0085] The A2 usage anomaly may correspond to: 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 make these adjustments, without which it will not be able to progress.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 resulting from an A22 control anomaly, i.e. inappropriate control of the crane by the crane operator (lifting a load too quickly, rotating the boom too quickly, etc.).

[0086] The climate anomaly A3 includes different subcategories of anomaly A31, A32 relating for example to the nature of the extreme climate condition: strong gusts of wind A31; heavy rain A32; very high or very low temperatures.

[0087] Finally, the organizational anomaly A4 may correspond to an activity anomaly A1 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 at all); or a management anomaly A2 due to an organizational failure in the management of the construction site, logistical delays, etc.

[0088] The decision criteria enabling 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.

[0089] 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.

[0090] Depending on his analysis needs, the user selects in the remote analysis system 6, during the second processing E5, the criterion(s) which appear to him to be the most relevant.

[0091] In one embodiment of the invention, an option is available in the remote analysis system 6 so that the user can define new decision criteria relating to an anomaly type A1, A2, A3, A4 and / or to an anomaly subcategory 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.

[0092] 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, D11, D12, DW and the environmental data D2 so that it identifies that the drop in activity is due to this new type of anomaly.

[0093] Two application examples of the diagnostic method 1 for the analysis of AP activity period analysis are presented and illustrated. Figure 3 and Figure 4 In these two examples, the activity of crane 2 is analyzed for AP activity periods corresponding to weeks spanning January 2019 and July 2020.

[0094] 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.

[0095] In reference to the Figure 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 prior to 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

[0096] 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 that week. If, conversely, the working time Ho of the week in question is less than the activity threshold, then a drop in activity is observed for that week, and the week is considered suspect.

[0097] Thus, a period of decreased LAP activity corresponds either to one suspicious week or to several consecutive suspicious weeks.

[0098] In order to determine the causes of the activity drops in the suspect weeks detected during the first E4 treatment, the working time Ho for each week / period of AP activity is compared with the crane data D1, D11, D12 associated with these suspect weeks during the second E5 treatment, which were also recorded for each week / period of AP activity.

[0099] 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 A1, more precisely to a material fault A11.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] After applying this decision criterion, 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 can have a single or multiple origins).

[0104] In reference to the Figure 4 , in the context of the second application example, the evolution of the crane's working time over the activity period is compared to a fixed activity threshold which is equal to 27 hours.

[0105] A period of decreased LAP activity is detected when the working time Ho becomes lower than the fixed activity threshold.

[0106] 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.

[0107] The crane data D1, D11, D12 considered during the second processing E5 for this second application example again corresponds to the number of lifting faults NG due to a malfunction of faulty equipment 31.

[0108] In order to determine whether the malfunction of the faulty equipment 31 is the cause of the drops in activity in the four activity periods, the following criterion is applied: if the number of NG lifting faults is non-zero during a period of LAP activity drop, then the malfunction of the equipment partly explains the drop in activity observed during this period of LAP activity drop.

[0109] 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.

[0110] On the other hand, no NG lifting fault was observed during the second and fourth periods of LAP activity decline, meaning that the malfunction of the faulty equipment 31 is not a cause of these. Thus, the diagnostic method 1 must relate other crane data D1, D11, D12 and / or the environmental data D2 with the working time Ho of the second and fourth periods of LAP activity decline to determine the causes explaining them.

[0111] 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.

[0112] In a first embodiment of the invention, the diagnostic method 1 implements, in parallel with the processing E4, E5 of the data D1, D11, D12, DW for each period of AP activity, a display of a progress of said processing E4, E5 on a screen integrated into the remote analysis system 6 or connected to it.

[0113] 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.

[0114] 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 format not currently taken into account by the diagnostic method 1 during the data formatting step E2.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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, D11, 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 anomaly subcategories A11, A12, A13, A21, A22, A31, A32, A41, A42 explaining them.

[0119] 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.

[0120] For example, in reference to the Figure 4is illustrated a hierarchical graph 100 called sunburst graph for a period of decreased LAP activity, with: the inner ring IR segmented into several pieces of inner ring IRP corresponding to anomalies A1, A2, A3, A4; and the outer ring OR segmented into several pieces of outer ring ORP corresponding to anomaly subcategories A11, A12, A13, A21, A22, A31, A32, A41, A42. The areas of each piece of inner ring IRP (respectively of each piece of outer ring ORP) are representative of the occurrence of anomaly A1, A2, A3, A4 (respectively of anomaly subcategory A11, A12, A13, A21, A22, A31, A32, A41, A42) during the period of decreased LAP activity.

[0121] Thus, if during a period of reduced LAP activity an anomaly A1, 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 outer ring pieces ORP will adapt accordingly (since it reflects an occurrence of anomaly A1, A2, A3, A4 or anomaly subcategory A11, A12, A13, A21, A22, A31, A32, A41, A42).

[0122] In the example given in Figure 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 on the crane or organizational shortcomings concerning the construction site (nearly 50% of the decline in crane activity is due to organizational anomaly A4).

[0123] In the example given in Figure 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, internal anomalies A3 are only due to software defects A12 and communication defects A13. The analysis of this second period of activity shows that the decline in activity of crane 2 is mainly due to usage anomalies A2 (notably piloting anomalies A22) and organizational anomalies A4.

[0124] 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

1. The diagnostic method (1) of an activity of a crane (2) for a detection and a classification of a drop in activity period (LAP) of said crane (2) in a construction site among several activity periods (AP), said diagnostic method (1) implementing at least the following steps: - detecting (E11) crane data (D1, D11, D12, DW) coming from equipment (3, 31) of the crane, and comprising at least work data (DW) representative of a crane work (2) implementing at least one maneuver of at least one structural element of the crane (2); - detecting (E12) environmental data (D2) representative of a construction site environment, and comprising at least climatic data; - logging (E3) by activity period (AP) of crane data (D1, D11, D12, DW) and environmental data (D2) in a remote database (4); - for each activity period (AP), processing (E4) the work data (DW) to calculate a work time (Ho) of the crane (2) during the activity period (AP), and comparison of said work time (Ho) with at least one activity threshold to determine whether said activity period (AP) is a drop in activity period (LAP) or not; - for each drop in activity period (LAP), processing (E5) crane data (D1, D11, D12, DW) and environmental data (D2) associated at least with said drop in activity period (LAP) to identify at least one anomaly (A1, A2, A3, A4) of the construction site or the crane (2), which anomaly (A1, A2, A3, A4) being associated with said drop in activity period (LAP).

2. The 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. The 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 crane data (D1, D11) from said faulty equipment (31).

4. The 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 use anomaly (A2) reflecting non-compliant use of the crane and identified from the crane data (D1, D11, D12, DW).

5. The diagnostic method (1) according to claim 4, wherein the at least one use anomaly (A2) comprises at least one mounting anomaly (A21) reflecting a mounting, or an adjustment, or both, of equipment non-compliant or not suitable for the construction site, and identified from sensor data (D12) selected from crane data (D1, D11, D12, DW) and coming from at least one sensor (5) of the crane (2).

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

7. The 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 climatic anomaly (A3) reflecting an extreme and identified climatic condition from climatic data selected from environmental data (D2).

8. The diagnostic method (1) according to any one of the preceding claims, wherein the climatic data comprise at least one of the following data: temperature data, wind speed data and hygrometric data.

9. The 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 crane usage and identified from crane data (D1, D11, D12, DW).

10. The diagnostic method (1) according to claim 9, wherein the at least one organizational anomaly (A4) is identified from at least one of the following data among the crane data (D1, D11, 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, cycle counting data load lifting, data representative of pause time between two maneuvers, data representative of types of maneuver, data representative of a crane (2) type.

11. The 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 work data (DW), crane data (D1, D11, D12, DW) and environmental data (D2) to determine whether each activity period (AP) is a drop in activity period (LAP) or not and to associate with each drop in activity period (LAP) the at least one corresponding anomaly (A1, A2, A3, A4).

12. The diagnostic method (1) according to claim 11, wherein the remote analysis system (6) structures the crane data (D1, D11, D12, DW) and the environmental data (D2) in a same predefined format.

13. The diagnostic method (1) according to any one of the preceding claims, wherein the diagnostic method (1) implements, subsequent to the processing (E4, E5) of crane data (D1, D11, D12, DW) and environmental data (D2) of each drop in activity period (AP), a generation (E6), or a display (E7), or both, of an analysis report comprising, for the or each drop in activity period (LAP), information specific to the at least one identified anomaly (A1, A2, A3, A4).

14. The diagnostic method (1) according to any one of the preceding claims, wherein, for each activity period (AP), the activity threshold for said activity period (AP) corresponds to an average value of the work time (Ho) of several activity periods (AP) before said activity period (AP), or after said activity period (AP), or both.

15. The diagnostic method (1) according to any one of the preceding claims, wherein the environmental data (D2) comprise, in addition to climatic data, topographical data representative of the construction site surrounding topography.