WO2021042233A1 - Système de diagnostic à distance, appareil et procédé pour outil électrique - Google Patents

Système de diagnostic à distance, appareil et procédé pour outil électrique Download PDF

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Publication number
WO2021042233A1
WO2021042233A1 PCT/CN2019/103962 CN2019103962W WO2021042233A1 WO 2021042233 A1 WO2021042233 A1 WO 2021042233A1 CN 2019103962 W CN2019103962 W CN 2019103962W WO 2021042233 A1 WO2021042233 A1 WO 2021042233A1
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WIPO (PCT)
Prior art keywords
power tool
status
remote diagnosis
data
time sequence
Prior art date
Application number
PCT/CN2019/103962
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English (en)
Inventor
Sudharsan DHANACHA NDRAN
Chenxu Wang
Original Assignee
Robert Bosch Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch Gmbh filed Critical Robert Bosch Gmbh
Priority to CN201980099943.5A priority Critical patent/CN114729959A/zh
Priority to PCT/CN2019/103962 priority patent/WO2021042233A1/fr
Priority to DE112019007527.6T priority patent/DE112019007527T5/de
Publication of WO2021042233A1 publication Critical patent/WO2021042233A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass
    • G01M99/005—Testing of complete machines, e.g. washing-machines or mobile phones
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P1/00—Details of instruments
    • G01P1/12—Recording devices
    • G01P1/127—Recording devices for acceleration values
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration

Definitions

  • the disclosure relates to the technical field of remotely diagnosing power tools, and in particular to a system, apparatus and method for remotely diagnosing a power tool.
  • a power tool may function as an apparatus for converting electrical power into mechanical power and is widely used in various fields of production and life. It will be extremely beneficial to monitor a power tool for the purpose of obtaining information such as tool status while the power tool is working.
  • a detection device is provided between a power line of a power tool and a power socket into which the power tool is inserted, for detecting a current or a voltage from the power line of the power tool.
  • a person or an organization, who manages the power tool may determine whether there is a failure of the power tool based on the detected current or voltage.
  • the above technical solution in the art may only determine whether the power tool has a failure, however, it cannot provide any other information about tool status of the power tool. Further, the above technical solution in the art is not widely applicable. For example, in a scenario of using power tools in a construction site, a worker in the construction site may know a failure of a power tool when he or she is aware of abnormal noises or abnormal vibrations or smokes from the power tool, however, a person or an organization, who manages the power tool, may not know the failure of the power tool.
  • a remote diagnosis apparatus for a power tool comprising: an obtaining unit configured to remotely obtain status parameters representing a working status of the power tool, the status parameters being detected by a sensor of the power tool, and the status parameters including a first time sequence representing that the working status evolves over time; an processing unit configured to process the status parameters to generate prediction data representing a prediction of a usage status of the power tool, the prediction data including a second time sequence representing that the usage status evolves over time; and a diagnosing unit configured to analyze the prediction data to generate a diagnosis result indicating the usage status of the power tool.
  • a remote diagnosis system for a power tool comprising a sensor installed in the power tool, the sensor being configured to sense status parameters representing a working status of the power tool; a remote diagnosis apparatus as described above, a obtaining unit of the remote diagnosis apparatus being configured to locate remotely from and communicatively couple with the sensor to receive the status parameters; a processing unit of the remote diagnosis apparatus being configured to generate prediction data presenting a prediction of a usage status of the power tool; and a diagnosis unit of the remote diagnosis apparatus being configured to generate a diagnosis result based on the prediction data; and a human machine interface configured to communicatively couple with the diagnosis unit for displaying the diagnosis result.
  • a remote diagnosis method for a power tool optionally performed by means of a remote diagnosis apparatus as described above and/or by means of a remote diagnosis system as described above, the method comprising the steps of: remotely obtaining status parameters representing a working status of the power tool, the status parameters being sensed by a sensor of the power tool, and the status parameters including a first time sequence represents that the working status evolves over time; processing the status parameters to generate prediction data representing a prediction of a usage status of the power tool, the prediction data including a second time sequence representing that the usage status evolves over time; and analyzing the prediction data to generate a diagnosis result indicating the usage status of the power tool.
  • the power tool can be monitored in real time and a diagnosing process for the power tool can be implemented by a diagnosis apparatus which is remote from the power tool.
  • the diagnosis result of the diagnosing process can be obtained based on parameters detected by a sensor of the power tool, and the diagnosis result indicates information about usage status of the power tool, such as total working time and workload of the power tool, carbon bush status, and operating way of handling the power tool. Further, the diagnosis result can be presented to a person or an organization who manages the power tool.
  • IoT Internet of Things
  • machine learning technique are used, which can enhance the efficiency and accuracy of the diagnosing process and reduce the cost for implementing the diagnosing process.
  • Figure 1 is a schematic block diagram of a remote diagnosis system for a power tool according to a possible embodiment of the disclosure
  • Figure 2 is a schematic block diagram of a remote diagnosis system for a power tool according to another possible embodiment of the disclosure
  • Figure 3 is a schematic block diagram of a remote diagnosis apparatus of the remote diagnosis system illustrated in Figure 1;
  • Figure 4 is a schematic block diagram of a human machine interface of the remote diagnosis system illustrated in Figure 2;
  • Figure 5 is a schematic flow chart of a remote diagnosis method for a power tool according to a possible embodiment of the disclosure.
  • the disclosure generally relates to a technical solution of remotely diagnosing a power tool. According to the technical solution of the disclosure, a diagnosis result indicating some aspects of the usage status of the power tool is obtained based on status parameters detected by a sensor of the power tool.
  • the diagnosis approach may be implemented to one power tool (which may be used in a plant) , and may also be implemented to multiple power tools (which may be used in a construction site) .
  • the types of the power tool (power tools) can be various, for example, the power tool (power tools) may be one of more angle grinder, electric drill and ribbon saw.
  • the state parameters represent working status of a power tool and the state parameters may be understood as parameters like noises and vibrations generated by the power tool during working, or temperatures of a part of the power tool and so on.
  • the state parameters in the disclosure should be understood as parameters which can represent working status of the power tool, and are not limited to special parameters.
  • the usage status of the power tool may be understood as whether or not the power tool works normally; workload of the power tool (for example, workload lever of the power tool) ; whether or not the power tool is used properly (for example, the way in which an operator uses the power can inflect the operator is a skilled worker or a beginner) and so on.
  • the usage status of the disclosure should be understood as conditions which can represent usage status of the power tool, and is not limited to special usage status.
  • the diagnosis result may be understood as a result indicating one or more aspects of the usage status of the power tool.
  • the diagnosis result indicating an aspect of workload may be “workload level being high” , “workload level being middle” or “workload level being low” .
  • FIG. 1 illustrates a remote diagnosis system 100 for a power tool 1 according to a possible embodiment of the disclosure.
  • the remote diagnosis system 100 includes a sensor 10, a remote diagnosis apparatus 30, a data storage apparatus 40 and a human machine interface (HMI) 50.
  • HMI human machine interface
  • the sensor 10 is installed in the power tool 1 for detecting status parameters which can represent states of the power tool 1 during working.
  • the sensor 10 may be implemented as an acoustic sensor for sensing noises generated by the power tool 1 and converting the sensed noises into audio signals.
  • the sensor 10 may also be implemented as an acceleration sensor for detecting accelerations of the power tool 1 for reflecting vibrations of the power tool 1.
  • the sensor 10 transfers the sensed status parameters to the remote diagnosis apparatus 30 via a wireless communication network.
  • the remote diagnosis apparatus 30 may be implemented in the cloud, for example, in a cloud server.
  • the sensor 10 transmits the status parameters to a gateway device 20 via a first wireless communication network, and then the gateway device 20 uploads the status parameters to the cloud such that the remote diagnosis apparatus 30 receives the sensed data (i.e., the status parameters) .
  • the first wireless communication network may be implemented as a private network (for example, private LoRaWAN network) .
  • a private network for example, private LoRaWAN network
  • the second wireless communication network may be implemented as a public network.
  • the remote diagnosis apparatus 30 processes the received status parameters to generate a diagnosis result indicating usage status of the power tool 1.
  • the remote diagnosis apparatus 30 may process the status parameters by means of a machine learning model which has been well trained and by means of reference data in a data storage apparatus 40.
  • the data storage apparatus 40 may comprise one or more databases for storing the reference data.
  • the reference data include reference information for using in the diagnosing process.
  • the data storage apparatus 40 and the remote diagnosis apparatus 30 may be implemented in separate cloud servers or in a common cloud server. The working principle and process of the remote diagnosis apparatus 30 will be described in the following text.
  • the human machine interface 50 is commutatively coupled with the remote diagnosis apparatus 30 to receive and display the diagnosis result, such that at the human machine interface 50, a client or a manager of the power tool 1 can know the usage status of the power tool 1.
  • the human machine interface 50 may be disposed in a human interface device (not shown) .
  • the human interface device may be implemented as a device including a Web Application and the Web Application has the human machine interface 50.
  • the human interface device may also be implemented as an intelligent mobile terminal including an Application and the Application has the human machine interface 50.
  • the power tool can be monitored in real time and a client or a manager of the power tool can know the usage status of the power tool through a remote delivery, which is useful for making decisions such as a prediction of life time and a maintenance schedule for the power tool 1.
  • the remote diagnosis apparatus 30 comprises an obtaining unit 31, a processing unit 32 and a diagnosing unit 33.
  • the obtaining unit 31 obtains status parameters representing working status of the power tool 1 from the sensor 10.
  • the status parameters may include information of the working status evolving over time.
  • the status parameters includes a first time sequence x (0) , x (1) ??x (t-1) , x (t) representing that the working status evolves over time, wherein each data point of the first time sequence represents a working status of the power tool 1 at a corresponding time.
  • the processing unit 32 processes the status parameters to generate prediction data which represent a prediction of usage status of the power tool 1.
  • the processing unit 32 may use a machine learning model which is good at dealing with time-dependency data to perform the processing process.
  • the machine learning model may be an artificial neural network which has been well trained.
  • the machine learning model is configured to process a model input, i.e. the first time sequence x (0) , x (1) whilx (t-1) , x (t) , to generate a model output, i.e. the prediction data.
  • the model output includes a second time sequence y (0) , y (1) , ...y (y-1) , y (t) representing that the usage status evolves over time, wherein each data point of the second time sequence represents a usage status of the power tool 1 at a corresponding time.
  • the machining learning model is configured to process the first time sequence such that the data of the second time sequence are obtained based on temporally corresponding data of the first time sequence and based on preceding data of the second time sequence.
  • the machine learning model is a recursive machine learning model and configured to process the data in the first time sequence one by one to generate the second time sequence y (0) , y (1) , ...y (y-1) , y (t) , wherein each data point of the second time sequence is based on a temporally corresponding data point of the first sequence and further based on an immediately preceding data point of the second time sequence.
  • the data point y (1) of the second time sequence is calculated based on both the temporally corresponding date x (1) of the first time sequence and the immediately preceding data point y (0) of the second time sequence.
  • the technical solution of the disclosure can predict usage status of the power tool from the sensed data.
  • the predicting process is executed by means of a machine learning model and thus time-dependency of the sensed data can be fully dig out, as a result, the date of the usage status of the power tool can be read out efficiently.
  • the diagnosing unit 33 may analyze the prediction data based on reference data stored in the data storage apparatus 40, so as to generate a diagnosis result indicating some aspects of the usage status of the power tool 1.
  • the aspects may be predetermined after taking into consideration of industrial demands.
  • the data storage apparatus 40 stores a large number of reference data including reference information of usage statuses of various power tools.
  • reference data Z (A) represent noise data generated by a A-type power tool (i.e., the type of this power tool is A) working for a certain time period, and the reference data Z (A) indicate a status of workload level being high.
  • the diagnosing unit 33 victorizes the second time sequence to generate a prediction vector and victorizes the reference data, which is comparable with the second time sequence, to generate a reference vector.
  • the diagnosing unit 33 calculates a vector distance between the prediction vector and the reference vector.
  • the diagnosing unit 33 then generates a diagnosis result indicating that the usage status of the power tool is identical with that indicated by the reference data when the vector distance is equal to or less than a first predetermined threshold.
  • the first threshold may be set empirically.
  • the reference data being comparable with the second time sequence may be understood as the two data have some same fixed parameters, for example, two comparable data may indicate same status aspects of two power tools with the same type.
  • the technical solution of the disclosure can obtain an accurate diagnosis result by means of a number of reference data. Moreover, it can embrace differences between individual power tools. For example, noises of two power tools, which work in the same environment with the same type and the same workload, may be different slightly. The difference can be fully embraced by means of setting a reasonable first threshold and thus an accurate diagnosis result can be obtained.
  • the embodiment of using the reference data may further comprise a process of removing invalid data.
  • the diagnosing unit 33 is configured to determine the second time sequence as invalid data when the vector distance is greater than or equal to a second predetermined threshold. When the second time sequence is determined as invalid data, the diagnosing unit 33 does not generate any diagnosis result. Then the invalid data may be deleted.
  • the reason of causing invalid data may be various, for example, error detection caused by a failure of the sensor 10 or distorted data caused by a temporary failure of the wireless communication network.
  • the embodiment of using the reference data may comprise a data cleaning process.
  • the reference data stored in the data storage apparatus 40 may be provided by different makers or different managers of the power tool. A maker will not wish others access their private date and thus set access permissions to their data. The data cleaning process solves this problem.
  • the data storage apparatus 40 includes a first database and a second database.
  • the first database stores all the reference data related with various power tools.
  • the diagnosis unit 33 needs to access reference data, it firstly judges whether the reference data are authorization data with respect to the power tool and control the authorization data to transfer to the second database. The diagnosis unit 33 then accesses the second database for the authorization data.
  • authorization data with respect to a power tool may be understood as the data having access permissions to the power tool.
  • the diagnosis unit 33 generates information for displaying at the human machine interface 50 and transmits the information to the human machine interface 50, for example via a wireless communication network.
  • the information at least includes the diagnosis result, and may also include an ID (identification) of the power tool.
  • the displayed information may not be stored at the human machine interface 50.
  • the displayed information may store in the data storage apparatus 40 as history data for future use.
  • the diagnosis result may be used as sample data for training the machine learning model, and the diagnosis result may be present again as history data.
  • the disclosure may further comprise the technical solutions for changing the frequency with which the sensor 10 senses the status parameters. Some examples of changing the frequency are described now.
  • a decision of whether the frequency to be changed is determined according to the diagnosis result.
  • the processing unit 32 of the remote diagnosis apparatus 30 determines whether the frequency is to be changed according to the diagnosis result.
  • the processing unit 32 determines that the frequency is to be changed, the processing unit 32 generates a control signal and transmits the control signal to the sensor 10 such that the sensor 10 changes the frequency under the control signal.
  • the senor may decrease the detecting frequency under the control signal and thus realize cost saving and environmental protection.
  • the remote diagnosis system 100 further comprises an environmental sensor (not shown) for sensing environmental parameters representing the environment around the power tool 1.
  • the power tool 1 comprises a control unit (not shown) and the control unit receives the sensed environmental parameters.
  • the control unit of the power tool 1 determines whether the frequency to be changed according to the sensed environmental parameters.
  • the control unit may be a micro-processor installed in the power tool 1. When the control unit determines that the frequency is to be changed, the control unit generates a control signal and transmits the control signal to the sensor 10 such that the sensor 10 changes the detecting frequency under the control signal.
  • the control unit can make a decision of increasing the detecting frequency locally.
  • the decision is made locally by a control unit of the power tool 1.
  • the frequency may be changed according to an instruction from the human machine interface 50.
  • the obtaining unit 31 of the remote diagnosis apparatus 30 obtains an instruction that instructs the sensor 10 to change the frequency from the human machine interface 50.
  • the processing unit 32 generates a control signal in response to the instruction and transmits the control signal to the sensor 10 such that the sensor 10 changes the frequency under the control signal.
  • Figure 2 illustrates a remote diagnosis system 200 for multiple power tools according to another possible embodiment of the disclosure.
  • the remote diagnosis system 200 is similar with the remote diagnosis system 100 except that the remote diagnosis system 200 has multiple sensors 1, 2, 3 which are installed in multiple power tools 10, 12, 14 respectively.
  • the remote diagnosis apparatus 30 of the remote diagnosis system 200 may be implemented similarly with that of the remote diagnosis system 100.
  • the obtaining unit 31 obtains the status parameters include parameters representing working statuses of the multiple power tools, and the status parameters are detected by the sensors 1-3 installed in the multiple power tools 10-14 respectively.
  • the processing unit 32 is configured to process the status parameters to generate prediction data representing predictions of usage statuses of the multiple power tools.
  • the diagnosing unit 33 is configured to analyze the prediction data to generate diagnosis results, each of which represents a diagnosis result indicating a usage status of one power tool of the multiple power tools.
  • the structure and principle of the remote diagnosis system 200 are similar with that of the remote diagnosis system 100, and thus related description above is also applicable here.
  • the human machine interface 50 displays diagnosis results of the multiple power tools. See Figure 4, the human machine interface 50 displays diagnosis results P11-P33 corresponding to power tools P1-P3 respectively.
  • the disclosure in another aspect relates to a remote diagnosis method 500 for a power tool, which can be performed by using the above described apparatus 30 and/or system 100, 200. For this reason, various features described above with reference to the apparatus 30 and system 100, 200 are also applicable in the method 500, and thus the description to them is omitted.
  • a remote diagnosis method 500 to a possible embodiment of the disclosure is schematically shown in Figure 5 and mainly comprises the steps described below.
  • step S510 status parameters representing a working status of the power tool are remotely obtained, the status parameters being sensed by a sensor of the power tool, and the status parameters including a first time sequence represents that the working status evolves over time;
  • step S520 the status parameters are processed to generate prediction data representing a prediction of a usage status of the power tool, the prediction data including a second time sequence represents that the usage status evolves over time.
  • step S530 the prediction data are analyzed to generate a diagnosis result indicating the usage status of the power tool.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un appareil de diagnostic à distance (30) pour un outil électrique (1, 2, 3) comprenant : une unité d'obtention (31) configurée pour obtenir à distance des paramètres d'état représentant un état de fonctionnement de l'outil électrique (1, 2, 3), les paramètres d'état étant détectés par un capteur (10, 12, 14) de l'outil électrique (1, 2, 3), et les paramètres d'état comprenant une première séquence temporelle indiquant que l'état de fonctionnement évolue dans le temps ; une unité de traitement (32) configurée afin de générer des données de prédiction représentant une prédiction de l'état d'utilisation de l'outil électrique (1, 2, 3), les données de prédiction comprenant une seconde séquence temporelle indiquant que l'état de fonctionnement évolue dans le temps ; et une unité de diagnostic (33) configurée pour analyser les données de prédiction afin de générer un résultat de diagnostic indiquant l'état d'utilisation de l'outil électrique (1, 2, 3).
PCT/CN2019/103962 2019-09-02 2019-09-02 Système de diagnostic à distance, appareil et procédé pour outil électrique WO2021042233A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201980099943.5A CN114729959A (zh) 2019-09-02 2019-09-02 用于电动工具的远程诊断设备、系统和方法
PCT/CN2019/103962 WO2021042233A1 (fr) 2019-09-02 2019-09-02 Système de diagnostic à distance, appareil et procédé pour outil électrique
DE112019007527.6T DE112019007527T5 (de) 2019-09-02 2019-09-02 Ferndiagnosesystem, Einrichtung und Verfahren für ein Elektrowerkzeug

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