Multi-sensor monitoring method and system and engineering machinery

Abstract

The invention provides a multi-sensor monitoring method and system and engineering machinery. The method comprises the following steps: determining a key part and an operation system according to the equipment type of the engineering machinery; deploying a plurality of sensors for monitoring and reflecting the states of the key parts and the characteristics of the operation system at the key parts and the operation system; establishing a correlation model among the sensor, the key part and the operation system; establishing a prediction model of each sensor according to the historical sensor normal data of the key part and the operation system in the normal use process to obtain prediction data; acquiring monitoring data of the plurality of sensors, and comparing and analyzing the monitoring data with the corresponding prediction data to determine abnormal data of the sensors; and based on the abnormal data of the sensor, according to the correlation model among the sensor, the key part and the operation system, determining the key part and / or the operation system with a fault, and obtaining a fault positioning result. Intelligent monitoring of the state of the engineering machinery is realized, and abnormal information of equipment can be timely and comprehensively output.

Description

Technical Field

[0001] The present invention relates to a multi-sensor monitoring method, system and construction machinery, belonging to the field of intelligent monitoring of construction machinery. Background Art

[0002] Construction machinery equipment (including products such as excavators, loaders, mining trucks, cranes, rollers, graders, etc.) and systems are rapidly developing towards the direction of intelligence and unmanned operation by combining existing software and hardware technologies. Equipment health, as the key to ensuring the safe operation of equipment and construction safety, is of great importance that cannot be ignored in the research and development of intelligence and unmanned related technologies. Using intelligent related technologies to realize the real-time monitoring of the equipment operation state and health state helps to comprehensively reflect the overall condition of the equipment, make predictions and maintenance in a timely manner, so as to improve construction safety, construction efficiency and reduce construction costs.

[0003] The existing equipment health and operation state monitoring technologies often rely on a single sensor, and the data analysis and parameter extraction are too simple to explain the abnormal conditions caused by environmental fluctuations, which easily lead to misjudgment and missed judgment. In recent years, with the continuous progress of sensor technology and the wide application of Internet of Things technology, multi-sensor monitoring technology has gradually become a new trend in the health and operation state monitoring of construction machinery equipment. The multi-sensor monitoring technology can realize the comprehensive monitoring of the health state and operation state of each system and key components in construction machinery equipment by integrating various different types of sensors, such as ultrasonic sensors, pressure / stress sensors, temperature sensors, vibration sensors, etc. These sensors can capture a variety of physical parameters of the equipment under different working conditions and operating conditions, such as ultrasonic parameters, pressure, stress, temperature, amplitude, etc., so as to provide more accurate and comprehensive data support for the state evaluation and fault diagnosis of the equipment, and thus guide the equipment for preventive maintenance and reliability assessment.

[0004] However, the multi-sensor monitoring technology also has difficult problems that need to be solved urgently. First of all, the increase in the number and types of sensors has increased the difficulty of data analysis and processing. How to efficiently filter out useless data and extract the key feature parameters of the data is a prerequisite for ensuring the accuracy and usability of the data. Secondly, the data collected by different sensors has heterogeneity. How to effectively integrate various types of data into the prediction model and warning system is the key problem that the multi-sensor monitoring technology needs to solve. Therefore, in view of the above technical problems, the present invention patent proposes a multi-sensor monitoring method, system, construction machinery and data processing method for the field of intelligent monitoring / detection of construction machinery, and establishes a comprehensive and real-time monitoring method and system for the health state and operation state of construction machinery.

[0005] The prior art mainly has three disadvantages. First, the monitoring scope is too single, only monitoring a certain key part or system, and the types of data obtained through monitoring are also too single, unable to conduct correlation analysis on multiple types of data. Second, only over-limit values are set in the abnormal data analysis, and it is impossible to exclude unexplained abnormal situations caused by environmental fluctuations. Third, the existing monitoring data analysis results cannot achieve in-depth analysis of the causes of faults, and cannot guide maintenance personnel in maintenance work and prevent the recurrence of the same fault. Summary of the Invention

[0006] The present invention provides a multi-sensor monitoring method, system and construction machinery, which solve the problems disclosed in the background art.

[0007] To solve the above technical problems, the present invention provides a multi-sensor monitoring method, including: Determine the key parts and operating systems according to the types of construction machinery and equipment; Deploy multiple sensors for monitoring the states of key parts and the characteristics of operating systems at the key parts and operating systems; Establish an association model among the sensors, key parts and operating systems; Establish a prediction model for each sensor based on the historical normal sensor data of the key parts and operating systems during normal use to obtain prediction data; Obtain the monitoring data of multiple sensors, compare and analyze the monitoring data with the corresponding prediction data to determine the abnormal sensor data; Based on the abnormal sensor data, determine the key parts and / or operating systems where faults occur according to the association model among the sensors, key parts and operating systems to obtain a fault location result.

[0008] The present invention also provides a controller, including: a memory; and a processor coupled to the memory, the processor being configured to execute the multi-sensor monitoring method as described above based on instructions stored in the memory.

[0009] The present invention also provides a multi-sensor monitoring system, including: the controller as described above, further including: a display and alarm module for displaying the abnormal sensor data and the fault location result and giving an alarm prompt.

[0010] The present invention also provides a construction machinery, including: the multi-sensor monitoring system or the controller as described above.

[0011] Advantages achieved by the present invention: The present invention covers the deployment of various sensors for engineering machinery and equipment; preprocesses the acquired data information; establishes a prediction model based on normal data; establishes an association model for sensors, key components, and operating systems; designs an anomaly index according to the prediction data output by the prediction model and the real-time monitoring data of sensors; outputs early warning information, fault location information, etc.; and conducts fault analysis and repair based on a fault expert database. The present invention uses the monitoring data of various sensors to realize the intelligent monitoring of the state of construction machinery, can comprehensively and timely output the abnormal information of the equipment, support the safe operation of intelligent / unmanned equipment, avoid accidents, and reduce maintenance costs. It has the following advantages: (1) Using multiple sensors to monitor key components and systems in real time can improve the detection rate of damage and faults, and avoid unexplained anomalies caused by environmental fluctuations, as well as misjudgment and missed judgment of damage and faults; (2) Based on the association model, data prediction model, and anomaly monitoring data analysis, it is possible to obtain information such as the location of damage and faults more efficiently and accurately, facilitating damage repair and fault handling; (3) Through the alarm function, the staff can be timely reminded of damage to key components and system failures, and timely handle them based on the fault expert database to avoid more serious accidents and losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of a multi-sensor monitoring system according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the process flow of a multi-sensor monitoring method according to an embodiment of the present invention; Figure 3 It is a schematic diagram of an association model of sensors, key components, and operating systems according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] The present invention will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0014] Embodiment 1: This embodiment provides a multi-sensor monitoring method, including: Determine key parts and operating systems according to the model of engineering machinery and equipment; Deploy multiple sensors for monitoring the state of key parts and the characteristics of operating systems in key parts and operating systems; Establish an association model among sensors, key parts, and operating systems; Establish a prediction model for each sensor based on the historical normal sensor data of key parts and operating systems during normal use to obtain prediction data; Obtain monitoring data from multiple sensors, compare and analyze the monitoring data with the corresponding prediction data, and determine abnormal sensor data; Based on the sensor abnormal data and the association model between the sensor, key parts and operating system, the key parts and / or operating system where the fault occurs are determined to obtain the fault location result.

[0015] In some embodiments, after obtaining the fault location result, it also includes: displaying sensor abnormal data and the fault location result, and issuing an alarm prompt.

[0016] In some embodiments, after obtaining the fault location result, the method further includes: determining a fault repair plan based on the fault location result and in combination with a fault expert database.

[0017] In some embodiments, the operating system includes a hydraulic system, an electrical system, and a transmission system.

[0018] In some embodiments, the sensor is selected from a plurality of sensors including ultrasound, pressure, stress, flow, temperature, rotation speed, vibration, current, voltage, and power sensors.

[0019] In some embodiments, acquiring monitoring data from multiple sensors further includes: Filter and denoise the collected raw sensor data to obtain monitoring data; Time series analysis technology is used to analyze the changing trends of monitoring data from different sensors over time, identify the time lag or synchronization relationship between monitoring data, and perform timestamp calibration on multiple sensors to eliminate time errors and deviations.

[0020] In some embodiments, the prediction data output by the prediction model is displayed in the form of a chart or a trend chart.

[0021] In some embodiments, this embodiment provides a multi-sensor monitoring method, such as Figure 2 As shown, specifically including: Step 1: Determine the type of construction machinery and equipment, and select the key parts and operating systems that need to be monitored through expert consultation, historical fault data analysis, and system structure analysis; Step 2: Select sensors that can reflect the status of key parts and the characteristics of the operating system. According to the monitoring parameters to be output, select the main parameters suitable for the sensor and control the cost. Step 3: Deploy corresponding sensors for multiple key monitoring locations or operating systems, and arrange them appropriately to ensure comprehensive monitoring data is obtained; Step 4: Conduct stability and availability tests on each sensor to ensure that it can accurately and stably receive data from key parts and operating systems to be monitored; Step 5: Use time series analysis techniques to study the variation trends of different sensor data over time, identify the time lags or synchronization relationships between the data, perform timestamp calibration on multiple sensors, and eliminate time errors and biases; Step 6: Process the collected raw data such as filtering and denoising to improve the accuracy and stability of the data; Step 7: Conduct a detailed analysis of the overall operation logic of the construction machinery components or systems to understand how each component works together, including analyses of aspects such as energy flow, signal transmission, and material circulation, so as to reveal the correlations between multiple key parts and operating systems, and thus establish the correlation relationships between the monitoring data of multiple sensors, as Figure 3 shown; Step 8: By calculating metrics such as the correlation coefficient or mutual information between different sensor data, quantify their linear or non-linear correlations, and thus reveal which parts or systems are interdependent in terms of state changes; Step 9: Adopt a multi-sensor data fusion algorithm to integrate the data from different sensors and generate more comprehensive and accurate information; Step 10: Perform statistical analysis, trend prediction, etc. on the fused data to discover the patterns and trends in the data; Step 11: Display the prediction analysis results in the form of charts, trend graphs, etc. to form a prediction model; Step 12: Input the data obtained by multiple sensors into the monitoring system, and then conduct a comparative analysis with the predicted data of the prediction model to accurately distinguish whether the abnormal data obtained by a certain sensor is caused by environmental fluctuations or a fault occurrence; Step 13: Based on the correlation relationships of multiple sensors and the correlation relationships between multiple key parts and operating systems, accurately determine which corresponding key part or operating system has a problem when the data of a certain sensor is abnormal, and further achieve fault location; Step 14: Analyze the causes of the fault based on the fault location results and multiple sensor data parameters, so as to guide the staff to carry out maintenance and component replacement work.

[0022] Embodiment 2: This embodiment provides a controller, including: a memory; and a processor coupled to the memory, the processor being configured to execute the multi-sensor monitoring method according to any one of Embodiment 1 based on the instructions stored in the memory.

[0023] Embodiment 3: This embodiment provides a multi-sensor monitoring system including the controller described above.

[0024] In some embodiments, it further includes a display and alarm module, which is configured to display abnormal sensor data and fault location results and give an alarm prompt. Figure 1 Schematic diagram of the multi-sensor monitoring system according to an embodiment of the present invention.

[0025] Embodiment 4: In some other embodiments of the present disclosure, there is also provided a construction machine, which includes the multi-sensor monitoring system in the above embodiments, or the above controller.

[0026] In some embodiments, the construction machine can be equipment such as an excavator, a loader, a mining truck, a crane, a roller, a grader, etc. The real-time monitoring of the health status and operating status of the construction machine is realized through the multi-sensor monitoring system.

[0027] In some other embodiments, a computer-readable storage medium stores computer program instructions, and when the instructions are executed by a processor, the steps of the methods in the corresponding embodiments above are implemented.

[0028] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a device, or a computer program product. Therefore, the present disclosure can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can adopt the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0029] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0030] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0031] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for the specified functions.

[0032] So far, the present disclosure has been described in detail. To avoid obscuring the concept of the present disclosure, some details well known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0033] The method and apparatus of the present disclosure can be implemented in many ways. For example, the method and apparatus of the present disclosure can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present disclosure are not limited to the specific order described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure can also be implemented as a program recorded in a recording medium, and these programs include machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0034] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for illustration and not for limiting the scope of the present disclosure. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A multi-sensor monitoring method, characterized in that: include: Determine key parts and operating systems according to the type of construction machinery and equipment; Deploy multiple sensors at key locations and operating systems to monitor and reflect the status of key locations and operating system characteristics; Establish the correlation model between sensors, key parts and operation systems; According to the historical sensor normal data of key parts and operating systems in normal use, the prediction model of each sensor is established to obtain the prediction data; Obtain monitoring data from multiple sensors, compare and analyze the monitoring data with the corresponding prediction data, and determine abnormal sensor data; Based on the sensor abnormal data and the association model between the sensor, key parts and operating system, the key parts and / or operating system where the fault occurs are determined to obtain the fault location result.

2. The multi-sensor monitoring method according to claim 1, characterized in that: After obtaining the fault location result, the method further includes: determining a fault repair plan according to the fault location result and in combination with a fault expert database.

3. The multi-sensor monitoring method according to claim 1, characterized in that: After obtaining the fault location result, it also includes: displaying sensor abnormal data and fault location result, and giving an alarm prompt.

4. The multi-sensor monitoring method according to claim 1, characterized in that: The operating system includes a hydraulic system, an electrical system and a transmission system.

5. The multi-sensor monitoring method according to claim 1, characterized in that: The sensor is selected from a variety of sensors including ultrasound, pressure, stress, flow, temperature, rotation speed, vibration, current, voltage, and power sensors.

6. The multi-sensor monitoring method according to claim 1, characterized in that: Acquire monitoring data from multiple sensors, including: Filter and denoise the collected raw sensor data to obtain monitoring data; Time series analysis technology is used to analyze the changing trends of monitoring data from different sensors over time, identify the time lag or synchronization relationship between monitoring data, and perform timestamp calibration on multiple sensors to eliminate time errors and deviations.

7. The multi-sensor monitoring method according to claim 1, characterized in that: The prediction data output by the prediction model is displayed in the form of charts and trend graphs.

8. A controller, characterized in that: include: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the multi-sensor monitoring method according to any one of claims 1 to 7 based on instructions stored in the memory.

9. A multi-sensor monitoring system, characterized in that: include: The controller according to claim 8, It also includes: a display alarm module, which is used to display sensor abnormal data and fault location results, and issue an alarm prompt.

10. An engineering machine comprising: The multi-sensor monitoring system as claimed in claim 9, or the controller as claimed in claim 8.

Images