A fault diagnosis system and method for a roller bed

Abstract

The application provides a fault diagnosis system and method for a roller bed, which comprises a monitoring robot and a server end; the server end is used for generating a target inspection path according to a digital twin model of the roller bed, and sending the target inspection path to the monitoring robot; the monitoring robot is used for driving to a target roller bed measuring point according to the received target inspection path, collecting a measuring point monitoring result of a roller bed rotating part corresponding to the target roller bed measuring point, and sending the measuring point monitoring result to the server end; the server end is also used for performing fault diagnosis on the roller bed according to the received measuring point monitoring result, and obtaining a fault diagnosis result; and the above system improves the efficiency and accuracy of roller bed fault diagnosis.

Description

Technical Field

[0001] This application relates to the field of fault diagnosis technology, and in particular to a fault diagnosis system and method for roller conveyors. Background Technology

[0002] Roller conveyors are core components of material handling systems, widely used in industries such as steel, mining, building materials, ports, and logistics, undertaking the continuous transfer of materials such as steel billets, ores, brick blanks, and bulk cargo. Taking the steel industry as an example, roller conveyor clusters operate under complex conditions of high-temperature heat radiation, iron oxide scale accumulation, and strong electromagnetic interference during core processes such as hot rolling and continuous casting. The operating status of their rotating components, such as bearings, shaft ends, and couplings, directly affects the safety and efficiency of the entire production line. However, under long-term heavy loads, speed changes, and impacts, the rotating components of roller conveyors are highly susceptible to various mechanical failures, including wear, pitting, fatigue, and misalignment. Since a single roller conveyor failure can cause material jamming or even unplanned shutdowns of the entire line, and these failures often lack obvious external symptoms in their early stages, failure to detect and locate them in time will lead to continuous deterioration of the faults, ultimately causing serious production accidents and economic losses.

[0003] In the fault diagnosis technology of related roller conveyors, the fixed sensor-based solution suffers from high deployment and maintenance costs, rigid fixed measuring points, and poor signal reliability in harsh environments; the manual inspection-based solution is limited by high-risk working environments and cannot achieve high-frequency, standardized close-range contact diagnosis. In other words, the existing technologies are difficult to achieve efficient and accurate fault diagnosis of roller conveyors, and there is an urgent need for a new fault diagnosis system and method for roller conveyors to solve the above problems.

[0004] It should be noted that the above content only provides background information related to this application and does not necessarily constitute prior art. Summary of the Invention

[0005] This application provides a fault diagnosis system and method for roller conveyors to solve the technical problem that it is difficult to achieve efficient and accurate fault diagnosis of roller conveyors in related technologies.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0007] This application provides a fault diagnosis system for roller conveyors, comprising a monitoring robot and a server. The server is configured to generate a target inspection path based on a digital twin model of the roller conveyor and send the target inspection path to the monitoring robot. The monitoring robot is configured to travel to a target roller conveyor measuring point according to the received target inspection path, collect the measuring point monitoring results of the roller conveyor rotating component corresponding to the target roller conveyor measuring point, and send the measuring point monitoring results to the server. The server is also configured to perform fault diagnosis on the roller conveyor based on the received measuring point monitoring results to obtain a fault diagnosis result.

[0008] In one embodiment of this application, based on the foregoing scheme, the monitoring robot includes a robotic arm, which is equipped with a sensor unit and a force feedback unit. The sensor unit includes an intrinsically safe stress wave sensor and / or an intrinsically safe vibration sensor. The robotic arm is used to collect the target bonding pressure between the sensor unit and the target roller conveyor measuring point using the force feedback unit, and after controlling the target bonding pressure within a preset pressure range, use the sensor unit to collect data on the roller conveyor rotating component corresponding to the target roller conveyor measuring point to obtain sensor signals.

[0009] In one embodiment of this application, based on the aforementioned scheme, the monitoring robot further includes an intrinsically safe communication module, an edge processing module, and a navigation module. The edge processing module and the sensor unit transmit signals via a shielded cable. The edge processing module is used to preprocess the sensor signals sent by the sensor unit and extract fault features to obtain fault feature data. It then performs lightweight processing on the fault feature data to obtain the measurement point monitoring results and sends these results to the server. The signal preprocessing includes digital filtering, noise suppression, and feature enhancement. The edge processing module is also used to perform preliminary fault identification based on the fault feature data and generate suspected fault warning information based on the obtained preliminary identification results. The communication module is used to transmit the measurement point monitoring results from the monitoring robot to the server and to transmit the target inspection path from the server to the monitoring robot. The server and the monitoring robot are connected via a network. The navigation module uses a simultaneous localization and mapping (SMR) navigation algorithm to guide the monitoring robot along the target inspection path to the target roller conveyor measurement point based on the digital twin model.

[0010] In one embodiment of this application, based on the aforementioned scheme, the server includes a path planning unit, a data mapping unit, and a fault analysis unit; the path planning unit is used to generate a target inspection path that meets preset requirements based on the operation constraint area marked in the digital twin model and the roller measuring point positions corresponding to each roller rotating component, combined with the roller running priority, and send the target inspection path to the monitoring robot, wherein the digital twin model is constructed based on the collected three-dimensional spatial data of the roller cluster, the roller cluster includes one or more rollers, and the preset requirements include shortest distance priority and obstacle avoidance priority; The data mapping unit is used to receive the robot position and the measurement point monitoring results sent by the monitoring robot, associate the measurement point monitoring results with the corresponding roller conveyor measurement points in the digital twin model, and synchronously display the measurement point monitoring results of each corresponding roller conveyor measurement point and the robot position on the digital twin model; the fault analysis unit is used to obtain the roller conveyor process parameters, perform fault diagnosis on the roller conveyor based on the roller conveyor process parameters and the measurement point monitoring results, obtain the fault diagnosis results, and generate early warning information based on the fault diagnosis results, wherein the roller conveyor process parameters include roller conveyor speed and load torque.

[0011] In one embodiment of this application, based on the aforementioned scheme, the server further includes a digital dashboard unit and a message push unit; the digital dashboard unit is used to acquire early warning information and fault location results, and centrally display the early warning information and the fault location results, wherein the fault location results are generated by associating the early warning information with the corresponding roller conveyor measurement point in the digital twin model through a data mapping unit; the message push unit is used to acquire the early warning information and push the early warning information to the operation and maintenance terminal, and the message push unit is communicatively connected to the operation and maintenance terminal.

[0012] This application also provides a fault diagnosis method for roller conveyors, applied on a server side. The fault diagnosis method for roller conveyors includes: generating a target inspection path based on a digital twin model of the roller conveyor, and sending the target inspection path to a monitoring robot; receiving the measurement point monitoring results sent by the monitoring robot, and performing fault diagnosis on the roller conveyor based on the measurement point monitoring results to obtain a fault diagnosis result, wherein the measurement point monitoring results are obtained by the monitoring robot traveling to the target roller conveyor measurement point according to the received target inspection path and collecting data from the roller conveyor rotating component corresponding to the target roller conveyor measurement point.

[0013] In one embodiment of this application, based on the aforementioned scheme, generating a target inspection path according to the digital twin model of the roller conveyor includes: collecting three-dimensional spatial data of the roller conveyor cluster; constructing the digital twin model based on the three-dimensional spatial data, wherein the roller conveyor cluster includes one or more roller conveyors; and generating a target inspection path that meets preset requirements based on the operation constraint areas marked in the digital twin model and the roller conveyor measuring point positions corresponding to the rotating components of each roller conveyor, combined with the roller conveyor operation priority, wherein the preset requirements include shortest distance priority and obstacle avoidance priority.

[0014] In one embodiment of this application, based on the aforementioned scheme, fault diagnosis of the roller conveyor is performed according to the monitoring results of the measuring points to obtain fault diagnosis results, including: acquiring roller conveyor process parameters, the roller conveyor process parameters including roller conveyor speed and load torque; and performing fault diagnosis of the roller conveyor based on the roller conveyor process parameters and the monitoring results of the measuring points to obtain the fault diagnosis results.

[0015] In one embodiment of this application, based on the aforementioned scheme, the method for obtaining the monitoring result of the measuring point includes: a robotic arm using a force feedback unit to collect the target bonding pressure between the sensor unit and the target roller conveyor measuring point, and controlling the target bonding pressure within a preset pressure range; then using the sensor unit to collect data from the roller conveyor rotating component corresponding to the target roller conveyor measuring point to obtain sensor signals; wherein the monitoring robot includes the robotic arm, and the robotic arm is equipped with the sensor unit and the force feedback unit; the sensor signals are preprocessed and fault features are extracted to obtain fault feature data; the fault feature data is then subjected to lightweight processing to obtain the monitoring result of the measuring point; wherein the signal preprocessing includes digital filtering, noise suppression, and feature enhancement.

[0016] In one embodiment of this application, after obtaining the fault diagnosis result based on the foregoing scheme, the method further includes at least one of the following: acquiring early warning information and fault location results, and centrally displaying the early warning information and the fault location results, wherein the early warning information is generated based on the fault diagnosis result, and the fault location result is generated by associating the early warning information with the corresponding roller conveyor measuring point in the digital twin model; acquiring the early warning information and pushing the early warning information to the operation and maintenance terminal; acquiring the robot position of the monitoring robot, associating the measuring point monitoring result with the corresponding roller conveyor measuring point in the digital twin model, and synchronously displaying the measuring point monitoring result of each corresponding roller conveyor measuring point and the robot position on the digital twin model.

[0017] The beneficial effects of this application are as follows: A fault diagnosis system for roller conveyors includes a monitoring robot and a server. The server generates a target inspection path based on a digital twin model of the roller conveyor and sends the target inspection path to the monitoring robot. The monitoring robot travels to the target roller conveyor measurement point according to the received target inspection path, collects the measurement point monitoring results of the roller conveyor rotating components corresponding to the target roller conveyor measurement point, and sends the measurement point monitoring results to the server. The server also performs fault diagnosis on the roller conveyor based on the received measurement point monitoring results to obtain fault diagnosis results. By collecting the measurement point monitoring results of the roller conveyor rotating components corresponding to the target roller conveyor measurement point through the monitoring robot, efficient and reliable monitoring of the operating status of the roller conveyor rotating components is achieved, which is of great significance for ensuring production safety and reducing maintenance costs. Timely and accurate fault diagnosis of the roller conveyor through the server is of vital importance for ensuring production continuity and reducing operation and maintenance costs.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] In the attached diagram: Figure 1 This is a schematic diagram of the architecture of a fault diagnosis system for roller conveyors, as illustrated in an exemplary embodiment of this application. Figure 2 This is a schematic diagram of the structure of a robotic arm shown in an exemplary embodiment of this application; Figure 3 This is a schematic diagram of the structure of a monitoring robot shown in an exemplary embodiment of this application; Figure 4 This is a schematic diagram of the server-side structure shown in an exemplary embodiment of this application; Figure 5 This is a schematic flowchart illustrating a fault diagnosis method for roller conveyors, as shown in an exemplary embodiment of this application. Detailed Implementation

[0021] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0024] First, it's important to clarify that intrinsically safe electrical equipment is a type of explosion-proof device that limits the energy of electrical sparks or thermal effects in a circuit, preventing the ignition of flammable gas mixtures in an explosive atmosphere. Its explosion-proof marking is "Ex i," and according to GB3836.4-2010 standards, it is divided into three levels: ia, ib, and ic. Level ia requires the equipment to maintain explosion-proof performance under normal operating conditions, single faults, and double faults, and is suitable for Zone 0 (the highest hazard area), offering the highest safety factor. This technology ensures that the energy generated by the equipment under normal or fault conditions is below the minimum ignition threshold of an explosive by controlling parameters such as voltage, current, capacitance, and inductance.

[0025] Laser-based SLAM (Simultaneous Localization and Mapping) navigation is a robot navigation technology that uses lidar data for real-time localization and map building, achieving centimeter-level or even millimeter-level high-precision positioning through sensor fusion. This technology requires no modification to the ground or deployment of auxiliary facilities; the robot can autonomously create maps and plan paths, and it has been widely applied in fields such as autonomous driving, industrial robots, and robotic vacuum cleaners.

[0026] Digital filtering (DF) is a technique that suppresses interference waves and highlights effective waves through spectral feature analysis. It is primarily used in seismic data processing and has been extended to fields such as speech, image, and biomedical signal processing. Its core principle is to separate signals from noise using algorithms. It is divided into two main categories: classical filtering based on Fourier analysis (low-pass, high-pass, band-pass, etc.) and modern filtering based on statistical methods (Wiener filtering, Kalman filtering).

[0027] Shielded cables are transmission lines that use a metal braided layer to wrap the signal lines. The braided layer is usually made of red copper or tin-plated copper.

[0028] Currently, fault diagnosis for roller conveyors mainly relies on the following two technical approaches: One approach is the fault diagnosis solution based on fixed sensors. This solution involves pre-embedding stress wave and vibration sensors at key measuring points such as bearing housings and shaft ends on each roller conveyor to continuously collect equipment operating status signals. Maintenance personnel periodically extract the data, or the system automatically collects the data for offline or online analysis to identify any abnormal characteristics. However, this solution has inherent drawbacks: Firstly, for large clusters containing hundreds or thousands of roller conveyors, the deployment of fixed sensors requires extensive upfront wiring, installation, commissioning, and protection work, resulting in extremely high initial costs. Secondly, due to the harsh operating environment of roller conveyors (high temperature, dust, splashes, strong electromagnetic interference), fixed sensors and their cables are easily damaged, leading to data acquisition interruptions or signal distortion, directly affecting the accuracy of fault diagnosis. Furthermore, subsequent maintenance is difficult and costly. In addition, once deployed, fixed measuring points are difficult to adjust, making it difficult to adapt to the differentiated diagnostic needs of different periods and different equipment.

[0029] Secondly, there is the fault diagnosis solution based on manual inspection. This solution relies on professional maintenance personnel carrying portable vibration and temperature measuring instruments to periodically approach each roller conveyor measuring point for contact measurements, and judge whether there are potential faults in the equipment based on the measurement data and experience. However, this solution is severely limited by the working environment—high-temperature heat radiation, iron oxide scale accumulation, slippery ground, and strong electromagnetic interference, which not only pose a significant threat to the personal safety of inspection personnel, but also makes it difficult to increase the frequency of inspections. Furthermore, the results of manual measurement are greatly affected by human factors such as operating techniques and deviations in the position of measuring points, making it difficult to guarantee the standardization and repeatability of diagnostic results.

[0030] In summary, among the fault diagnosis technologies for related roller conveyors, fixed sensor-based solutions suffer from high deployment and maintenance costs, rigid and fixed measuring points, and poor signal reliability in harsh environments. Manual inspection-based solutions are limited by high-risk operating environments and cannot achieve high-frequency, standardized, close-range contact-based diagnosis. Therefore, these technologies struggle to achieve efficient and accurate fault diagnosis of rotating components in roller conveyor clusters, necessitating a novel fault diagnosis solution that can overcome the impact of harsh operating conditions while balancing economic efficiency and diagnostic accuracy.

[0031] To address the aforementioned problems, embodiments of this application propose a fault diagnosis system for roller conveyors and a fault diagnosis method for roller conveyors, which will be described in detail below.

[0032] Figure 1 This is a schematic diagram illustrating the architecture of a fault diagnosis system for roller conveyors, as shown in an exemplary embodiment of this application. Figure 1 As shown, in an exemplary embodiment, a fault diagnosis system for roller conveyors includes a monitoring robot 101 and a server 102. The server 102 is used to generate a target inspection path based on a digital twin model of the roller conveyor and send the target inspection path to the monitoring robot 101. The monitoring robot 101 is used to travel to the target roller conveyor measuring point according to the received target inspection path, collect the measuring point monitoring results of the roller conveyor rotating components corresponding to the target measuring point, and send the measuring point monitoring results to the server 102. The server 102 is also used to perform fault diagnosis on the roller conveyor based on the received measuring point monitoring results to obtain a fault diagnosis result. It is understood that there are one or more target roller conveyor measuring points.

[0033] Figure 2 This is a schematic diagram of the structure of a robotic arm shown in an exemplary embodiment of this application, with reference to... Figure 2 As shown, in an exemplary embodiment, the monitoring robot 101 includes a robotic arm 1011, which is equipped with a sensor unit 10112 and a force feedback unit 10111. The sensor unit 10112 includes an intrinsically safe stress wave sensor and / or an intrinsically safe vibration sensor. The robotic arm 1011 is used to collect the target bonding pressure between the sensor unit 10112 and the target roller conveyor measuring point using the force feedback unit 10111, and after controlling the target bonding pressure within a preset pressure range, it uses the sensor unit 10112 to collect data from the roller conveyor rotating component corresponding to the target roller conveyor measuring point to obtain sensor signals.

[0034] Figure 3 This is a schematic diagram of the structure of a monitoring robot shown in an exemplary embodiment of this application, with reference to... Figure 3As shown, in an exemplary embodiment, the monitoring robot 101 further includes an intrinsically safe communication module 1013, an edge processing module 1012, and a navigation module 1014. The edge processing module 1012 and the sensor unit 10112 transmit signals via a shielded cable. The edge processing module 1012 performs signal preprocessing and fault feature extraction on the sensor signals sent by the sensor unit 10112 to obtain fault feature data. It then performs lightweight processing on the fault feature data to obtain the measurement point monitoring results and sends these results to the server 102. The signal preprocessing includes digital filtering, noise suppression, and... Feature enhancement; edge processing module 1012 is also used to perform preliminary fault identification based on fault feature data, and generate fault suspicion warning information based on the obtained preliminary identification results; communication module 1013 is used to transmit the measurement point monitoring results from the monitoring robot 101 to the server 102, and transmit the target inspection path from the server 102 to the monitoring robot 101, wherein the server 102 and the monitoring robot 101 are connected through a network; navigation module 1014 is used to construct a navigation algorithm using simultaneous positioning and mapping, and guide the monitoring robot 101 to move along the target inspection path to the target roller conveyor measurement point based on a digital twin model.

[0035] Figure 4 This is a schematic diagram of the server-side structure shown in an exemplary embodiment of this application, with reference to... Figure 4 As shown, in an exemplary embodiment, the server 102 includes a path planning unit 1021, a data mapping unit 1022, and a fault analysis unit 1023. The path planning unit 1021 is used to generate a target inspection path that meets preset requirements based on the work constraint area marked in the digital twin model and the roller measuring point positions corresponding to each roller rotating component, combined with the roller running priority, and send the target inspection path to the monitoring robot 101. The digital twin model is constructed based on the collected three-dimensional spatial data of the roller cluster, which includes one or more rollers. The preset requirements include the shortest... Distance priority and obstacle avoidance priority are implemented; the data mapping unit 1022 is used to receive the robot position and measurement point monitoring results sent by the monitoring robot 101, associate the measurement point monitoring results with the corresponding roller conveyor measurement points in the digital twin model, and synchronously display the measurement point monitoring results and robot position of each corresponding roller conveyor measurement point on the digital twin model; the fault analysis unit 1023 is used to obtain the roller conveyor process parameters, perform fault diagnosis on the roller conveyor based on the roller conveyor process parameters and measurement point monitoring results, obtain fault diagnosis results, and generate early warning information based on the fault diagnosis results, wherein the roller conveyor process parameters include roller conveyor speed and load torque.

[0036] Continue to refer to Figure 4As shown, in an exemplary embodiment, the server 102 further includes a digital dashboard unit 1024 and a message push unit 1025; the digital dashboard unit 1024 is used to acquire early warning information and fault location results, and centrally display the early warning information and fault location results, wherein the fault location results are generated by associating the early warning information with the corresponding roller measurement points in the digital twin model through the data mapping unit 1022; the message push unit 1025 is used to acquire early warning information and push the early warning information to the operation and maintenance terminal, and the message push unit 1025 is connected to the operation and maintenance terminal.

[0037] In some embodiments, the monitoring robot is a tracked robot, the sensor unit includes an intrinsically safe stress wave sensor and / or an intrinsically safe vibration sensor, and the communication module is an intrinsically safe 5G (5th Generation Mobile Communication) system for industrial use. Taking the fifth-generation mobile communication technology (5G) communication module and server as an example, the roller conveyor digital twin monitoring platform (hereinafter referred to as the monitoring platform) is used. The tracked robot is equipped with an adaptive pressure control robotic arm to achieve 7N-8N pressure bonding to the measuring points of the equipment (i.e., the target roller conveyor measuring points). The system constructs a three-level electromagnetic anti-interference system of sensor signal shielded transmission + edge intelligent computing processing + intrinsically safe 5G private network transmission. The sensor collects stress wave and / or vibration signals of the rotating parts of the roller conveyor. The signal is processed by the edge processing module on the monitoring robot to reduce data weight, and then transmitted to the digital twin monitoring platform by the 5G communication module on the monitoring robot. After completing this three-level anti-interference processing, the platform constructs a three-dimensional model of the roller conveyor cluster, i.e., the digital twin model of the roller conveyor. It maps the robot position and the measuring point monitoring results in real time. Combined with the roller conveyor speed, load torque and other roller conveyor process parameters, it completes the equipment fault diagnosis and displays the early warning information and fault location results centrally through the digital dashboard unit.

[0038] In some embodiments, the fault diagnosis system for the roller conveyor includes: Tracked robot, explosion-proof rating is Ex db IIC T6 Gb level: The robot is equipped with an adaptive pressure control robotic arm. A force feedback unit is installed at the end of the robotic arm. Based on the target contact pressure collected by the force feedback unit between the end of the robotic arm and the target roller conveyor measuring point, the target contact pressure is adjusted to a preset pressure range (e.g., 7N-8N). It can be understood that "Ex" is an explosion-proof mark, indicating that the equipment meets explosion-proof standards; "db" is the explosion-proof type - flameproof type, which is the core safety guarantee, with d representing flameproof type and b representing high-level protection; "IIC" is the gas group, representing the gas categories in which the equipment can be safely used, with II referring to explosive gas environments other than coal mines, and C being the highest level, covering all gases of IIA and IIB, including the most dangerous hydrogen and acetylene; "T6" is the temperature group, with T6 being the highest safety level in the temperature group, indicating that the equipment's maximum surface temperature does not exceed 85℃ under any operating condition; "Gb" is the equipment protection level, where "Gb" represents a high protection level, suitable for explosive gas environments where explosive gases may or may occasionally appear during normal operation.

[0039] Intrinsically safe stress wave sensors and / or intrinsically safe vibration sensors are integrated into the end of a robotic arm to collect stress wave and / or vibration signals of the rotating components of the roller conveyor corresponding to the target roller conveyor measuring point, obtain sensor signals, and have explosion-proof certification to meet the safety requirements of industrial explosion-proof areas. The edge processing module, deployed inside the robot, is used for real-time digital filtering, noise suppression, fault feature enhancement, and preliminary fault identification of sensor signals from the rotating roller components. After completing the lightweight data processing, the obtained fault feature data is uploaded to the monitoring platform, providing an effective data foundation for the fault analysis unit of the monitoring platform. An intrinsically safe 5G communication module for factory areas, deployed inside the robot, is adapted to the safety requirements of explosion-proof Zone 1 and / or Zone 2 areas. It supports bidirectional transmission of monitoring results from measurement points and the target inspection path, with data transmission latency <50ms. This enables the monitoring platform to send the target inspection path to the robot in real time, and to transmit the monitoring results from measurement points, including roller conveyor status data, back to the robot in real time. Furthermore, when multiple monitoring robots are conducting collaborative inspections, the 5G communication module supports parallel transmission, improving collaborative inspection efficiency. It can be understood that, according to national standards, explosive gas environments are divided into three zones based on their hazard level: Zone 0: Locations where explosive gas environments are continuously present or exist for extended periods; Zone 1: Locations where explosive gas environments may occur during normal operation; Zone 2: Locations where explosive gas environments are unlikely to occur during normal operation, or if they do occur, it is only occasional and short-lived. Simply put, Zone 1 and Zone 2 are areas other than Zone 0 that pose an explosion risk, with Zone 1 having a higher risk and Zone 2 a relatively lower risk.

[0040] The roller conveyor digital twin monitoring platform constructs a 3D digital twin model of the roller conveyor cluster. The monitoring platform includes a path planning unit, a data mapping unit, a fault analysis unit, a digital dashboard unit, and a message push unit. The path planning unit generates the target inspection path for the monitoring robot based on the digital twin model. The data mapping unit correlates the robot's position with the monitoring results of the measuring points in real time. The fault analysis unit combines roller conveyor process parameters such as roller speed and load torque with the monitoring results of the measuring points to complete equipment fault diagnosis. The digital dashboard unit centrally displays early warning information and fault location results. The message push unit pushes early warning information to the operation and maintenance terminal.

[0041] In other embodiments, to be applicable to roller conveyor monitoring in industries such as steel, mining, building materials, ports, and logistics, especially for high-value scenarios of roller conveyor clusters in hot rolling and continuous casting processes in the steel industry and roller conveyor clusters in explosion-proof Zone 1 and / or Zone 2 areas of various industries, this application constructs a three-level electromagnetic interference resistance system of sensor signal shielded transmission + edge intelligent computing processing + intrinsically safe 5G private network transmission, which effectively resists strong electromagnetic interference in industrial sites.

[0042] This embodiment takes the hot rolling mill exit roller cluster in the steel industry as the application scenario. This scenario includes more than 200 rollers, distributed in the high-temperature and strong electromagnetic interference operation area of ​​the hot rolling workshop. Some areas are explosion-proof Zone 1 or Zone 2 areas. The site is adjacent to the main rolling mill frequency conversion drive system, and there is high-frequency harmonic and strong alternating magnetic field interference. Iron oxide scale is accumulated on the ground, and some areas are oily and slippery. The rotating parts of the rollers are prone to failures such as shaft head wear and bearing pitting.

[0043] The monitoring system deployed in this embodiment includes: one tracked robot, an intrinsically safe stress wave sensor and / or an intrinsically safe vibration sensor, an edge processing module, an intrinsically safe 5G communication module for the factory area, and a digital twin monitoring platform for the roller conveyor, as detailed below: This embodiment employs a customized tracked robot with an IP66 protection rating, suitable for the complex environment of a hot rolling mill, including dust, iron oxide scale splashes, and small amounts of water. It can also operate within explosion-proof Zone 1 and / or Zone 2 areas. The robot's walking speed is 0-1.3 m / s, and its runtime is ≤5 hours, meeting the needs of cluster inspection of the entire workshop's roller conveyor system. It's understood that IP66 means the equipment is completely dustproof and can withstand strong water spray, making it suitable for outdoor or dusty and humid environments.

[0044] The robot integrates a six-degree-of-freedom impact-resistant robotic arm, a laser SLAM navigation module (i.e., a navigation module), and a force feedback unit.

[0045] The six-degree-of-freedom impact-resistant robotic arm integrates a force feedback unit at its end effector and adopts a PID (Proportional-Integral-Derivative) control algorithm, which can stably adjust the target contact pressure to 7.5N (within the optimal range of 7N-8N), avoiding signal distortion or surface damage to the measuring point caused by improper pressure.

[0046] The laser SLAM navigation module is equipped with a SLAM mapping and positioning algorithm (i.e., simultaneous localization and map building navigation algorithm), which is suitable for the dim and obstacle-filled environment of the hot rolling workshop. Based on the digital twin model, it achieves accurate autonomous navigation, and the positioning accuracy meets the precise contact requirements of the roller bearing seat measuring point.

[0047] The sensor unit in this embodiment includes an intrinsically safe stress wave sensor and / or an intrinsically safe vibration sensor. The sensors are explosion-proof certified. The frequency response range of the stress wave sensor is 36KHz to 40KHz, and the frequency response range of the vibration sensor is 0.2Hz to 12KHz. The sensors are integrated into the end of the robotic arm and are in close contact with the measuring point of the roller bearing seat. They collect the stress wave characteristic signals and vibration characteristic signals of wear and pitting failure of rotating parts to obtain sensor signals, which are suitable for the monitoring needs of ordinary areas and explosion-proof Zone 1 and / or Zone 2 areas in the hot rolling workshop.

[0048] The edge processing module is built into the robot body. The signal transmission between the sensor unit and the edge processing module uses a shielded cable. The edge processing module incorporates digital filtering, noise suppression, and fault feature enhancement algorithms to eliminate electromagnetic interference noise in the hot rolling mill in real time, thus completing the preprocessing of the sensor signals. Fault features are extracted from the preprocessed sensor signals to obtain effective fault feature data. Based on the fault feature data, preliminary fault identification is performed, and a fault suspicion warning message is generated and output based on the preliminary identification results. At the same time, the fault feature data is processed to obtain the measurement point monitoring results, which are then uploaded to the monitoring platform. The data volume is compressed to 1 / N of the original data through the lightweight processing, where N is a positive integer not less than 500, which can provide an effective data foundation for process linkage analysis on the platform.

[0049] In this embodiment, a 5G private network has been deployed within the factory area, adopting a standalone (SA) networking mode. The intrinsically safe 5G communication module is built into the robot body, adapting to the safety requirements of explosion-proof Zone 1 and / or Zone 2 areas. It is equipped with a directional radio frequency antenna and configured with an anti-electromagnetic interference enhancement algorithm, with data transmission latency stabilizing at around 35ms. It achieves full signal coverage in the entire hot rolling workshop area and explosion-proof Zone 1 and / or Zone 2 areas, supports bidirectional data transmission between the robot and the roller conveyor digital twin monitoring platform, and can realize parallel transmission of multiple robots. The characteristic data transmission time of a single robot is ≤10ms.

[0050] The roller conveyor digital twin monitoring platform is based on the Unity3D engine to build a three-dimensional digital twin model of the hot rolling mill exit roller conveyor cluster. The monitoring platform is deployed on the plant server and specifically includes a path planning unit, a data mapping unit, a fault analysis unit, a digital dashboard unit, and a message push unit.

[0051] The path planning unit inputs information such as the bearing housing coordinates, equipment spacing, high-temperature restricted areas, and explosion-proof Zone 1 and / or Zone 2 boundaries for over 200 roller conveyors. It automatically generates the optimal target inspection path. A single robot can complete the inspection of the entire workshop's roller conveyors in ≤4 hours. The inspection frequency can be adjusted based on the roller conveyor operation priority (e.g., the core roller conveyor at the finishing mill exit is inspected every 8 hours, and the ordinary conveyor roller conveyor is inspected every 16 hours), generating the final target inspection path. It is understood that the roller conveyor measurement point locations include the bearing housing coordinates, and the operational constraint areas include the high-temperature restricted areas and explosion-proof Zone 1 and / or Zone 2 areas.

[0052] The data mapping unit links the robot's position with the fault warning information of each roller conveyor displayed synchronously on the digital twin model in real time, generating a visual monitoring dashboard that allows maintenance personnel to remotely view the equipment status.

[0053] In the fault analysis unit, real-time roller conveyor process parameters such as roller conveyor speed and load torque are input and combined with the measurement point monitoring results uploaded by the edge processing module to complete equipment fault diagnosis, accurately distinguishing between mechanical faults of the roller conveyor itself (such as bearing pitting and shaft head wear) and abnormal vibrations caused by process overload (such as stress waves and / or vibration exceeding the threshold caused by excessive load and sudden speed change).

[0054] The message push unit is integrated with the plant operation and maintenance management platform. The accurate early warning information output by the fault analysis unit is simultaneously pushed to the operation and maintenance personnel's terminals (such as mobile APP and computer terminal), which clearly indicates the location of the faulty roller conveyor, the fault type and handling suggestions.

[0055] This application constructs a three-level electromagnetic interference immunity system consisting of sensor signal shielded transmission, edge intelligent computing processing, and intrinsically safe 5G private network transmission. Its features include: The first level of anti-interference is the sensor signal shielding transmission layer: the intrinsically safe stress wave sensor and / or intrinsically safe vibration sensor are connected to the edge processing module through a shielded cable to suppress conducted electromagnetic interference during signal transmission, improve the anti-interference and security of the original signal transmission, and ensure the purity of the original signal. The second level of anti-interference is the edge intelligent computing processing layer: the edge processing module has built-in digital filtering, noise suppression, and fault feature enhancement algorithms to process the collected sensor signals in real time, eliminate signal distortion caused by on-site radiated electromagnetic interference, and accurately extract effective fault features; The third level of anti-interference is the intrinsically safe 5G private network transmission layer: The intrinsically safe 5G communication module in the factory area adopts an explosion-proof intrinsically safe design, equipped with a directional radio frequency antenna, and combined with the exclusive frequency band allocation of the factory area's independent 5G private network and anti-electromagnetic interference enhancement algorithm, it effectively resists radio frequency electromagnetic interference.

[0056] Figure 5 This is a schematic flowchart illustrating a fault diagnosis method for roller conveyors, as shown in an exemplary embodiment of this application. This fault diagnosis method for roller conveyors is applied on a server side and can be executed by a computing processing device. (Refer to...) Figure 5 As shown, the fault diagnosis method for roller conveyors includes at least steps S510 to S520, which are described in detail below: In step S510, a target inspection path is generated based on the digital twin model of the roller conveyor, and the target inspection path is sent to the monitoring robot.

[0057] In one embodiment of this application, generating a target inspection path based on a digital twin model of the roller conveyor includes: collecting three-dimensional spatial data of the roller conveyor cluster; constructing a digital twin model based on the three-dimensional spatial data, wherein the roller conveyor cluster includes one or more roller conveyors; and generating a target inspection path that meets preset requirements based on the operation constraint areas marked in the digital twin model and the roller conveyor measuring point positions corresponding to the rotating components of each roller conveyor, combined with the roller conveyor operation priority, wherein the preset requirements include shortest distance priority and obstacle avoidance priority.

[0058] In step S520, the monitoring results of the measurement points sent by the monitoring robot are received, and the fault diagnosis of the roller conveyor is performed based on the monitoring results to obtain the fault diagnosis results.

[0059] Among them, the measurement point monitoring results are obtained by the monitoring robot traveling to the target roller conveyor measurement point according to the received target inspection path and collecting data on the roller conveyor rotating components corresponding to the target roller conveyor measurement point.

[0060] In one embodiment of this application, fault diagnosis of the roller conveyor is performed based on the monitoring results of the measuring points to obtain fault diagnosis results, including: acquiring roller conveyor process parameters, which include roller conveyor speed and load torque; and performing fault diagnosis of the roller conveyor based on the roller conveyor process parameters and the monitoring results of the measuring points to obtain fault diagnosis results.

[0061] In one embodiment of this application, the method for obtaining the measurement point monitoring result includes: a robotic arm using a force feedback unit to collect the target bonding pressure between the sensor unit and the target roller conveyor measurement point, and after controlling the target bonding pressure within a preset pressure range, the sensor unit collects data from the roller conveyor rotating component corresponding to the target roller conveyor measurement point to obtain sensor signals. The monitoring robot includes a robotic arm, which is equipped with a sensor unit and a force feedback unit. The sensor signals are preprocessed and fault features are extracted to obtain fault feature data. The fault feature data is then subjected to lightweight processing to obtain the measurement point monitoring result. The signal preprocessing includes digital filtering, noise suppression, and feature enhancement.

[0062] In one embodiment of this application, after obtaining the fault diagnosis result, the method further includes at least one of the following: acquiring early warning information and fault location results, and centrally displaying the early warning information and fault location results, wherein the early warning information is generated based on the fault diagnosis result, and the fault location result is generated by associating the early warning information with the corresponding roller conveyor measurement point in the digital twin model; acquiring the early warning information and pushing the early warning information to the operation and maintenance terminal; acquiring the robot position of the monitoring robot, associating the measurement point monitoring result with the corresponding roller conveyor measurement point in the digital twin model, and synchronously displaying the measurement point monitoring result and robot position of each corresponding roller conveyor measurement point on the digital twin model; receiving the user's verification result of the early warning information, if the verification result is a correct diagnosis, then after the user performs maintenance on the corresponding roller conveyor rotating component, the corresponding roller conveyor rotating component is re-diagnosed to obtain a new fault diagnosis result; if the verification result is a wrong diagnosis, then the fault diagnosis result is updated according to the user's diagnosis result.

[0063] In some embodiments, still taking a tracked robot as the monitoring robot, a roller conveyor digital twin monitoring platform as the server, sensor units including intrinsically safe stress wave sensors and / or intrinsically safe vibration sensors, and a communication module as an intrinsically safe 5G communication module for the factory area as an example, the fault diagnosis method for roller conveyors includes the following steps: Step 1: Construct a three-dimensional digital twin model of the roller conveyor cluster based on the digital twin map, and input the roller conveyor measuring point positions and operation constraint areas of each roller conveyor rotating component. The roller conveyor measuring point positions include measuring point coordinates, and the operation constraint areas include hazardous operation areas and navigation restricted areas. The roller conveyor rotating components include roller conveyor bearings, shaft heads, couplings, gearboxes, and drive motor bearings, etc. Step 2: The path planning unit of the roller conveyor digital twin monitoring platform generates the robot's autonomous inspection path, i.e. the target inspection path, and sends it to the tracked robot. Step 3: The robot navigates along the target inspection path to the target roller conveyor measuring point. After the robotic arm adjusts the target contact pressure fed back by the force feedback unit to the preset pressure range (e.g., 7N-8N), it controls the sensor unit to make close contact with the target roller conveyor measuring point. Step 4: The intrinsically safe stress wave sensor and / or intrinsically safe vibration sensor collect stress wave and / or vibration signals, which are transmitted to the edge processing module through primary anti-interference (shielded cable transmission). The edge processing module performs secondary anti-interference (digital filtering, noise suppression, fault feature enhancement) and fault feature extraction to obtain fault feature data. The fault feature data is then processed to reduce its size, and the obtained measurement point monitoring results are output (the data volume is compressed to 1 / N of the original data, where N is a positive integer not less than 500). Step 5: The measurement point monitoring results output by the edge processing module are transmitted to the roller conveyor digital twin monitoring platform via a 5G communication module with three levels of anti-interference (intrinsically safe 5G private network); Step 6: The data mapping unit associates the monitoring results of the measurement points with the corresponding roller conveyor measurement points in the digital twin model. The fault analysis unit combines the roller conveyor process parameters to diagnose equipment faults and identify fault types such as shaft head wear, bearing pitting, and coupling misalignment. Step 7: Display the early warning information and fault location results centrally through the digital dashboard unit, and push the early warning message to the operation and maintenance terminal through the message push unit.

[0064] The tracked robot in this embodiment possesses excellent terrain mobility and anti-jamming characteristics, making it suitable for complex environments such as accumulated iron oxide scale around the roller conveyor and slippery, oily surfaces. The tracked robot is also equipped with a laser SLAM navigation module, forming dual navigation redundancy with a digital twin map to improve navigation reliability in complex environments. Furthermore, the monitoring platform communicates with the roller conveyor management system to acquire roller conveyor process parameters such as roller speed and load torque.

[0065] In other embodiments, the fault diagnosis method for roller conveyors is implemented based on a fault diagnosis system for roller conveyors, and the specific steps are as follows: 1. Digital Twin Model Construction. Using laser scanning technology, three-dimensional spatial data of the hot rolling mill's exit roller cluster was collected. Combined with information from the equipment logbook, including the model, parameters, and bearing housing coordinates of over 200 rollers, a 1:1 three-dimensional digital twin model was constructed in the Unity3D engine. Boundaries were delineated for a high-temperature restricted area, an oxide scale accumulation hazard area, and explosion-proof Zone 1 and / or Zone 2 areas, marking these as robot restricted or limited-access areas, i.e., operational constraint zones.

[0066] 2. Target Inspection Path Planning and Distribution: The path planning unit of the roller conveyor digital twin monitoring platform generates the target inspection path based on the operation constraint area, the location of the roller conveyor measuring points, and the roller conveyor operation priority (core roller conveyor at the finishing mill exit > ordinary conveyor roller conveyor). The path planning follows the principle of "shortest distance + obstacle avoidance priority" to avoid the robot crossing high-temperature and high-risk areas, while adapting to the operation requirements of explosion-proof Zone 1 and / or Zone 2 areas. After the planning is completed, the target inspection path is distributed to the tracked robot through the intrinsically safe plant 5G communication module.

[0067] 3. The robot autonomously navigates and aligns with the target roller conveyor measurement point. After receiving the target inspection path, the robot starts from the charging base station and autonomously travels to the target roller conveyor measurement point based on the digital twin map using the laser SLAM navigation module. Upon arrival, the robotic arm adjusts its posture according to the measurement point coordinates in the digital twin model. The force feedback unit detects the target alignment pressure in real time and adjusts the target alignment pressure to 7.5N through PID closed-loop control. This ensures that the control sensor unit makes close contact with the roller conveyor at the target roller conveyor measurement points such as the bearing seat.

[0068] 4. Sensor data acquisition and edge intelligent processing: The intrinsically safe stress wave / vibration sensor acquires stress wave / vibration signals at a time of 10 seconds per measurement point. After acquisition, the data is transmitted to the edge processing module via a shielded cable. The module performs signal filtering, amplification, fault feature extraction, and lightweight processing of the raw data into feature data.

[0069] 5. Feature data transmission and platform joint diagnosis: The edge processing module transmits the processed feature data to the roller conveyor digital twin monitoring platform through the intrinsically safe 5G communication module in the factory area, with a transmission latency of 35ms. The fault analysis unit combines the real-time speed, load torque and other process parameters of the roller conveyor to complete the equipment fault diagnosis and distinguish between equipment faults and process overload abnormalities.

[0070] 6. Accurate fault early warning and message push: The data mapping unit associates the fault diagnosis results with the corresponding roller measuring points in the digital twin model and displays the early warning information synchronously on the digital twin model, such as "The head of the G-058 shaft of the finishing mill exit roller has a wear defect, and the remaining life is about 135 days"; the message push unit pushes the early warning information synchronously to the operation and maintenance personnel's operation and maintenance terminal.

[0071] 7. Fault verification and closed-loop processing: During the planned maintenance period, the operation and maintenance personnel inspected the shaft head of roller conveyor G-058 based on the early warning information and found that there was a wear defect in the shaft head, which was consistent with the system monitoring results. After the maintenance personnel performed maintenance on the shaft head, the robot monitored the roller conveyor again. The fault analysis unit determined that the equipment had returned to normal, completed the fault closed-loop processing, and updated the equipment status information.

[0072] In some embodiments of this application, when the ground is flat, the tracked robot can be replaced with a wheeled robot to suit roller conveyor monitoring on flat ground. The three-level anti-interference system adapts to the electromagnetic interference characteristics of this scenario, and the algorithm parameters of the edge processing module and the 5G private network frequency band can be adjusted to maintain the anti-interference effect. This application can also be extended to industrial roller conveyor cluster monitoring in other industries such as conveyor roller conveyors in mining and beneficiation plants, glass annealing kilns in the building materials industry, bulk cargo loading roller conveyors in ports, and automated warehouse sorting roller conveyors in the logistics industry. It can adapt to both ordinary environments and explosion-proof Zone 1 and / or Zone 2 areas. The three-level anti-interference system can effectively resist electromagnetic interference generated by equipment such as frequency converters, high-voltage cabinets, and motors in various scenarios. In addition, according to the roller conveyor monitoring needs of different industries, a temperature sensor can be added to the stress wave and / or vibration sensor to achieve multi-parameter joint acquisition of temperature and stress wave and / or vibration signals.

[0073] This embodiment takes a hot rolling mill roller cluster in the steel industry as an example. This cluster contains more than 200 rollers and is a core material conveying component at the outlet of the hot rolling mill. A single roller failure will cause unplanned shutdown of the hot rolling production line, resulting in direct losses exceeding 5 million yuan per day. Compared with fixed sensor solutions, the single tracked robot in this application can achieve full coverage monitoring of the entire roller cluster in the workshop, reducing the operational risks for equipment management and maintenance personnel in high-temperature, strong electromagnetic interference, and explosion-proof high-risk environments. The initial investment in the system is ≤5% of that of the fixed sensor solution. The configuration of intrinsically safe sensors and intrinsically safe 5G communication modules allows the system to be adapted to both ordinary industrial environments and explosion-proof high-risk environments, greatly improving industry adaptability.

[0074] This application constructs a three-level electromagnetic interference suppression system consisting of sensor signal shielded transmission, edge intelligent computing processing, and intrinsically safe 5G private network transmission. This system achieves full-link interference suppression for signal acquisition, processing, and transmission. The three-level system works in a progressive and synergistic manner, effectively resisting strong electromagnetic interference generated by frequency converters and electromagnetic stirring devices in scenarios such as hot rolling and mining. It also solves the problems of poor signal-to-noise ratio and difficulty in fault feature extraction in related technologies, and significantly improves the accuracy of monitoring data.

[0075] The edge processing module of this application completes real-time preprocessing and preliminary fault identification at the front end, effectively eliminating electromagnetic interference noise on site, achieving data lightweighting, ensuring 5G transmission efficiency and the effectiveness of platform-side diagnostic data. Combined with multi-parameter joint diagnosis of the fault analysis unit, it can accurately distinguish between equipment faults and process overload anomalies, effectively reducing false alarm and missed alarm rates, identifying early fault characteristics of roller conveyor rotating parts in advance, avoiding unplanned equipment downtime, with a reasonable input-output ratio and significant economic and social benefits.

[0076] It should be noted that the fault diagnosis method for roller conveyors provided in the above embodiments and the fault diagnosis system for roller conveyors provided in the above embodiments belong to the same concept. The specific methods of performing each step have been described in detail in the system embodiments and will not be repeated here. In practical applications, the fault diagnosis method for roller conveyors provided in the above embodiments can be assigned to different steps as needed, and this is not a limitation here.

[0077] The flowcharts, block diagrams, and other schematic diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the systems and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0078] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0079] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0080] Other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and the disclosed implementations. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed in this application.

[0081] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A fault diagnosis system for roller conveyors, characterized in that, This includes monitoring robots and server-side components; The server is used to generate a target inspection path based on the digital twin model of the roller conveyor and send the target inspection path to the monitoring robot. The monitoring robot is used to travel to the target roller conveyor measuring point according to the received target inspection path, collect the measuring point monitoring results of the roller conveyor rotating component corresponding to the target roller conveyor measuring point, and send the measuring point monitoring results to the server. The server is also used to perform fault diagnosis on the roller conveyor based on the received monitoring results of the measurement points, and obtain fault diagnosis results.

2. The fault diagnosis system for roller conveyors according to claim 1, characterized in that, The monitoring robot includes a robotic arm, which is equipped with a sensor unit and a force feedback unit. The sensor unit includes an intrinsically safe stress wave sensor and / or an intrinsically safe vibration sensor. The robotic arm is used to collect the target bonding pressure between the sensor unit and the target roller conveyor measuring point using the force feedback unit, and after controlling the target bonding pressure within a preset pressure range, it uses the sensor unit to collect data from the roller conveyor rotating component corresponding to the target roller conveyor measuring point to obtain sensor signals.

3. The fault diagnosis system for roller conveyors according to claim 2, characterized in that, The monitoring robot also includes an intrinsically safe communication module, an edge processing module, and a navigation module. The edge processing module and the sensor unit use a shielded cable for signal transmission. The edge processing module is used to perform signal preprocessing and fault feature extraction on the sensor signals sent by the sensor unit to obtain fault feature data, perform lightweight processing on the fault feature data to obtain the measurement point monitoring result, and send the measurement point monitoring result to the server. The signal preprocessing includes digital filtering, noise suppression and feature enhancement. The edge processing module is also used to perform preliminary fault identification based on the fault feature data, and generate suspected fault warning information based on the obtained preliminary identification results; The communication module is used to transmit the monitoring results of the measurement points from the monitoring robot to the server, and to transmit the target inspection path from the server to the monitoring robot, wherein the server and the monitoring robot are connected via a network. The navigation module is used to construct a navigation algorithm using synchronous positioning and mapping, and based on the digital twin model, guide the monitoring robot to move along the target inspection path to the target roller conveyor measuring point.

4. The fault diagnosis system for roller conveyors according to any one of claims 1 to 3, characterized in that, The server-side includes a path planning unit, a data mapping unit, and a fault analysis unit; The path planning unit is used to generate a target inspection path that meets preset requirements based on the operation constraint area marked in the digital twin model and the roller measuring point positions corresponding to each roller rotating component, combined with the roller running priority, and send the target inspection path to the monitoring robot. The digital twin model is constructed based on the collected three-dimensional spatial data of the roller cluster, the roller cluster includes one or more rollers, and the preset requirements include shortest distance priority and obstacle avoidance priority. The data mapping unit is used to receive the robot position and the measurement point monitoring results sent by the monitoring robot, associate the measurement point monitoring results with the corresponding roller conveyor measurement points in the digital twin model, and synchronously display the measurement point monitoring results of each corresponding roller conveyor measurement point and the robot position on the digital twin model. The fault analysis unit is used to acquire the roller conveyor process parameters, perform fault diagnosis on the roller conveyor based on the roller conveyor process parameters and the monitoring results of the measuring points, obtain the fault diagnosis results, and generate early warning information based on the fault diagnosis results. The roller conveyor process parameters include roller conveyor speed and load torque.

5. The fault diagnosis system for roller conveyors according to claim 4, characterized in that, The server also includes a digital dashboard unit and a message push unit; The digital dashboard unit is used to acquire early warning information and fault location results, and centrally display the early warning information and fault location results. The fault location results are generated by associating the early warning information with the corresponding roller measuring points in the digital twin model through a data mapping unit. The message push unit is used to obtain the warning information and push the warning information to the operation and maintenance terminal. The message push unit and the operation and maintenance terminal are connected in communication.

6. A fault diagnosis method for roller conveyors, characterized in that, Applied to the server side, the fault diagnosis method for roller conveyors includes: The target inspection path is generated based on the digital twin model of the roller conveyor, and the target inspection path is sent to the monitoring robot. The system receives the monitoring results of the measurement points sent by the monitoring robot, performs fault diagnosis on the roller conveyor based on the monitoring results, and obtains the fault diagnosis results. The monitoring results are obtained by the monitoring robot traveling to the target roller conveyor measurement point according to the received target inspection path and collecting data on the roller conveyor rotating component corresponding to the target roller conveyor measurement point.

7. The fault diagnosis method for roller conveyors according to claim 6, characterized in that, The target inspection path is generated based on the digital twin model of the roller conveyor, including: Collect three-dimensional spatial data of the roller conveyor cluster, and construct the digital twin model based on the three-dimensional spatial data, wherein the roller conveyor cluster includes one or more roller conveyors; Based on the work constraint areas marked in the digital twin model and the roller measuring point positions corresponding to each roller rotating component, and combined with the roller running priority, a target inspection path that meets preset requirements is generated, wherein the preset requirements include shortest distance priority and obstacle avoidance priority.

8. The fault diagnosis method for roller conveyors according to claim 6, characterized in that, Based on the monitoring results of the aforementioned measurement points, fault diagnosis is performed on the roller conveyor to obtain fault diagnosis results, including: Obtain the roller conveyor process parameters, including roller conveyor speed and load torque; Based on the roller conveyor process parameters and the monitoring results of the measurement points, the roller conveyor is fault diagnosed to obtain the fault diagnosis results.

9. The fault diagnosis method for roller conveyors according to any one of claims 6 to 8, characterized in that, The methods for obtaining the monitoring results of the measurement points include: The robotic arm uses a force feedback unit to collect the target bonding pressure between the sensor unit and the target roller conveyor measuring point, and controls the target bonding pressure within a preset pressure range. Then, it uses the sensor unit to collect data from the roller conveyor rotating component corresponding to the target roller conveyor measuring point to obtain sensor signals. The monitoring robot includes the robotic arm, which is equipped with the sensor unit and the force feedback unit. The sensor signal is preprocessed and fault feature is extracted to obtain fault feature data. The fault feature data is then subjected to lightweight processing to obtain the monitoring result of the measurement point. The signal preprocessing includes digital filtering, noise suppression, and feature enhancement.

10. The fault diagnosis method for roller conveyors according to any one of claims 6 to 8, characterized in that, After obtaining the fault diagnosis results, the method further includes at least one of the following: Acquire early warning information and fault location results, and centrally display the early warning information and the fault location results, wherein the early warning information is generated based on the fault diagnosis results, and the fault location results are generated by associating the early warning information with the corresponding roller measuring points in the digital twin model; Obtain the warning information and push it to the operation and maintenance terminal; The robot position of the monitoring robot is obtained, the monitoring results of the measurement points are associated with the corresponding roller conveyor measurement points in the digital twin model, and the monitoring results of each corresponding roller conveyor measurement point and the robot position are displayed synchronously on the digital twin model.

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