Intelligent carrier roller Internet of Things monitoring system based on self power supply and rotor sensing

Abstract

The invention relates to the technical field of carrier roller Internet of Things monitoring, in particular to an intelligent carrier roller Internet of Things monitoring system based on self-power supply and rotor sensing, and the system comprises a plurality of integrated monitoring terminals and an Internet of Things monitoring platform which comprises a management module, a deviation judgment module, an abnormality recognition module and an early warning module. According to the method, mechanical energy of carrier roller rotation is directly converted into electric energy, characteristic values such as vibration intensity and rotor rotating speed are extracted locally in real time through edge calculation, each terminal is endowed with a unique identity, remote secure firmware updating is supported, preliminary judgment is carried out through the matching degree of the real-time characteristic values and faults, and the fault diagnosis accuracy is improved. And then spatial clustering is carried out on a determination result, trend change characteristics are deeply excavated, early hidden dangers presenting a collaborative deterioration trend are identified, and the problems of low monitoring work stability and low fault positioning accuracy caused by difficult long-term power supply and difficult mass node management are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of idler roller Internet of Things monitoring, and particularly relates to an intelligent idler roller Internet of Things monitoring system based on self-power supply and rotor sensing. BACKGROUND

[0002] As a key supporting component of a belt conveyor, an idler roller is widely used in bulk material conveying systems such as coal mines, ports and power plants. Its running state is directly related to the safety and stability of the conveying system. If the idler roller runs poorly due to bearing wear, lubrication failure or structural damage, it will not only cause abnormal friction between the belt and the idler roller, resulting in increased energy consumption and reduced conveying efficiency, but also may cause high temperature rise of the idler roller, belt burnout and even fire and other serious safety accidents. Therefore, how to efficiently and accurately monitor and early identify the running state of the idler roller has become a key problem in intelligent operation and maintenance of the conveying system.

[0003] Chinese patent application publication No. CN217200404U discloses an intelligent iron idler roller, which comprises a roller skin, an idler roller shaft and an intelligent detection system arranged in a bearing seat. The intelligent detection system comprises a self-power generation module and a signal sending module. The self-power generation module comprises a stator sleeved on the idler roller shaft and a rotor connected to the roller skin. The stator and the rotor produce relative motion. The signal sending module comprises an intelligent mainboard connected to the stator and an antenna penetrating through the idler roller shaft. One end of the antenna is connected to the intelligent mainboard, and the other end extends to the end of the idler roller shaft.

[0004] It can be seen that the intelligent iron idler roller has the following problems: the internal installation environment of the idler roller is special, the space is limited and power supply is difficult under rotation, and traditional battery power supply cannot meet the long-term operation requirement; the number of idler rollers deployed is large, the positions are dispersed, the wireless communication environment on site is complex, signal attenuation, network congestion or data loss are prone to occur; it is difficult to realize systematic management and control of the whole life cycle of large-scale operation and maintenance for a large number of idler roller nodes. SUMMARY

[0005] Therefore, the present application provides an intelligent idler roller Internet of Things monitoring system based on self-power supply and rotor sensing, which overcomes the problems of low monitoring stability and low fault positioning accuracy in the prior art due to long-term power supply difficulty and mass node management difficulty by means of self-power supply mechanism, ID binding mechanism and multi-dimensional parameter analysis.

[0006] To achieve the above-mentioned purpose, the present application provides an intelligent idler roller Internet of Things monitoring system based on self-power supply and rotor sensing, which comprises: a plurality of integrated monitoring terminals built in the end covers of each to-be-measured idler roller, comprising: a self-power supply unit for self-power supply according to the rotating mechanical energy of the to-be-measured idler roller; The collection processing unit is used to collect temperature and vibration acceleration, and to perform edge computing processing on the vibration acceleration to obtain vibration intensity and rotor speed; The identification unit is used to store the identity of each to-be-tested carrier roller; The upgrade unit is used to send a query request and receive and execute an upgrade instruction; The Internet of Things monitoring platform is connected with all the integrated monitoring terminals, and comprises: The management module is used to obtain the running state data of each to-be-tested carrier roller including the temperature, the vibration intensity and the rotor speed, to establish a location mapping database according to the identity and the physical location of each to-be-tested carrier roller, and to generate and send the upgrade instruction according to the firmware check result in response to the query request; The deviation determination module is used to determine a number of state deviation carrier rollers according to the matching degree of the running state data and a preset abnormal state library; The abnormality identification module is used to identify a number of abnormal carrier rollers according to the trend correlation degree of the carrier rollers in a deviation cluster, wherein the trend correlation degree is determined based on the time sequence correlation characteristics of the running state data between each to-be-tested carrier roller and each state deviation carrier roller in the deviation cluster, and the deviation cluster is determined based on the spatial distribution characteristics of each state deviation carrier roller in the location mapping database; The early warning module is used to generate an abnormal early warning report according to all the abnormal carrier rollers.

[0007] Further, the self-powered unit comprises a stator assembly and a rotor assembly, the rotor assembly rotates with the to-be-tested carrier roller and comprises a coil winding, and the stator assembly comprises a permanent magnet and a counterweight which remains relatively stationary under the action of gravity; when the to-be-tested carrier roller rotates, the coil winding cuts the magnetic induction lines generated by the permanent magnet to generate an induced electromotive force, converting mechanical energy into electrical energy to power the integrated monitoring terminal.

[0008] Further, the vibration intensity is obtained according to the root mean square value of the tangential acceleration component within a preset observation time, wherein the tangential acceleration is obtained by coordinate transformation and decomposition of the vibration acceleration.

[0009] Further, the rotor speed is obtained based on the unit conversion of the main frequency component of the tangential acceleration component, wherein the main frequency component is obtained based on the frequency spectrum analysis of the tangential acceleration component and the extraction of the frequency component formed by the periodic projection of the gravity acceleration.

[0010] Further, the identity is obtained by scanning the RFID tag of each to-be-tested carrier roller, and the identity is bound in one-to-one correspondence with a preset structured physical location code to establish the location mapping database.

[0011] Further, the integrated monitoring terminal is timed to wake up according to a preset wake-up period, and sends a firmware version query request to the IoT monitoring platform at a preset specific wake-up time. The IoT monitoring platform generates and sends the upgrade instruction to the corresponding integrated monitoring terminal in response to the query request when it is determined that there is a new version of available firmware.

[0012] Further, the deviation determination module comprises: a deviation calculation unit configured to construct a real-time state vector according to the temperature, the vibration intensity, and the rotor speed, and to calculate a plurality of matching degrees according to the real-time state vector and each abnormal state vector in the preset abnormal state library; a deviation determination unit connected to the deviation calculation unit and configured to determine that the to-be-tested roller is the state deviation roller when the maximum value of all the matching degrees is greater than a preset matching threshold.

[0013] Further, based on the position mapping database, the physical position coordinates of each state deviation roller are obtained, and spatial clustering analysis is performed on all the physical position coordinates to obtain a plurality of deviation clusters.

[0014] Further, the roller trend correlation degree is calculated based on the trend similarity between the to-be-tested roller and each state deviation roller in the deviation cluster, wherein the trend similarity is calculated based on a to-be-tested vector set of the to-be-tested roller and a state deviation vector set of each state deviation roller within a preset identification time length.

[0015] Further, when the roller trend correlation degree is greater than a preset correlation threshold, the to-be-tested roller and the state deviation roller are determined to be the abnormal roller, and when the roller trend correlation degree is less than or equal to the preset correlation threshold, the state deviation roller is determined to be the abnormal roller.

[0016] Compared with the prior art, the beneficial effects of the present application are that, by constructing a complete intelligent chain from the intelligent sensing terminal to the cloud analysis and decision, the mechanical energy of the roller rotation is directly converted into electric energy at the terminal side, realizing the long-term operation of the terminal, the vibration intensity and rotor speed characteristic values are extracted in real time locally through edge computing, greatly reducing the load and delay of wireless data transmission, giving each terminal a unique identity, laying the foundation for accurate management, supporting remote security firmware updates, and ensuring the sustainable evolution of massive terminal functions. Based on the reliable data stream and identity coordinates provided by the terminal, the Internet of Things monitoring platform performs single-point preliminary screening through real-time data and historical fault mode matching, quickly captures significant abnormalities; then based on the accurate location information, the abnormal points are spatially clustered to filter out isolated false positives and locate the real problem area; finally, the trend is dug in the problem area, and through time series correlation analysis, early hidden dangers that have not yet reached the threshold but have shown a coordinated deterioration trend are identified. The system solves the systematic bottleneck of traditional monitoring methods in power supply, communication, positioning and warning accuracy, realizes the fundamental change from passive response to active prediction of the state of large-scale roller clusters, and effectively solves the problems of low stability and low fault positioning accuracy of monitoring work caused by long-term power supply difficulties and massive node management difficulties.

[0017] Further, by establishing a relative motion reference frame through a constant gravity physical field, irregular roller rotating mechanical energy is efficiently converted into electric energy. The stator assembly remains stationary relative to the ground under the action of the counterweight, while the rotor assembly rotates synchronously with the roller. A stable relative cutting motion is formed between the two, ensuring that the coil winding can continuously and smoothly cut the magnetic induction lines of the permanent magnet at any speed and installation angle, generating a stable induced electromotive force. This provides a continuous, stable and maintenance-free energy source for the entire integrated monitoring terminal, fundamentally ensuring that temperature, vibration acceleration and other raw data can be continuously and reliably collected. It is the physical cornerstone of the long-term, automatic and uninterrupted operation of the entire system data chain, making large-scale, unattended intelligent monitoring possible.

[0018] Further, the tangential component is accurately separated from the original acceleration signal through coordinate transformation. This component directly reflects abnormal friction and impact in the roller rotation plane, and is highly related to bearing wear and lubrication failure. Subsequently, the root mean square value of the tangential acceleration within a predetermined time period is calculated. The root mean square value, as a statistical quantity representing the power of the vibration signal, can effectively integrate the vibration energy within that time period, smoothing out transient interference and random noise while stably reflecting the trend of deterioration, providing a sensitive and reliable quantitative input for subsequent fault matching and trend analysis.

[0019] Furthermore, by utilizing the constant physical reference frame of gravity, the MEMS accelerometer is transformed into a virtual speed sensor that requires no additional hardware. Since the accelerometer rotates synchronously with the idler roller, the projection of gravitational acceleration on its tangential measurement axis will change periodically with the rotation angle. This frequency of change strictly corresponds to the rotation frequency of the idler roller. By performing spectral analysis on the tangential acceleration component, the main frequency component with a very high signal-to-noise ratio formed by the gravitational projection can be accurately separated from the complex vibration signal. This achieves the acquisition of key speed information with high reliability and low cost without adding any dedicated speed measuring components, simply by intelligently mining existing sensor data. This not only saves hardware costs and space, but also avoids speed measurement failures caused by the failure of additional sensors, enabling speed monitoring and vibration monitoring to achieve essential unity and mutual verification in terms of data source.

[0020] Furthermore, by transforming the disordered installation in the physical world into a precisely traceable ordered relationship in the digital world, the core pain point of industrial field equipment—identifiable but not locatable—is addressed. In actual installation, smart idlers are often not deployed in a fixed order. While this non-standardized approach increases installation flexibility, it also presents challenges in maintenance, especially when an idler malfunctions. Simply relying on the equipment ID makes it difficult to quickly locate its specific physical position within the conveyor system, thus affecting diagnostic and maintenance efficiency. By assigning each idler a unique RFID identifier—its name in the digital system—and a structured physical location code—its address in physical space—the system establishes an immutable one-to-one correspondence between name and address through on-site scanning and binding. This strongly correlates abstract status data with specific spatial coordinates. When any idler malfunctions, the system can not only report who is causing the problem but also immediately pinpoint its location, directly mapping data alarms into executable action commands. This achieves a closed loop from status monitoring to precise maintenance, significantly improving the management efficiency and fault response speed of large-scale distributed assets.

[0021] Furthermore, by decoupling and optimizing the scheduling of high-frequency, low-latency status monitoring data streams and low-frequency, delayable maintenance command streams in the time dimension, the conventional preset wake-up cycle is dedicated to ensuring the periodic collection and reporting of status data; while special preset specific wake-up times are dedicated to initiating maintenance queries on demand, with a rhythm far lower than the monitoring cycle, ensuring the lowest power consumption monitoring mode. Only in a very few predictable times will additional resources be consumed for health checks. The subsequent high-energy-consuming upgrade process is triggered only when the platform confirms that there is new firmware. This achieves the separation of business data and maintenance data and on-demand transmission, achieving a perfect balance of the three key requirements of endurance, real-time monitoring, and software maintainability at the system level. It is the core design that supports the autonomous operation of ultra-large-scale IoT nodes throughout their entire lifecycle.

[0022] Furthermore, by employing a multidimensional, decorrelation-free, and scale-uniform measurement method, the overall similarity between the current operating state and historical typical fault modes is quantified. Temperature, vibration intensity, and rotor speed—three parameters with different physical meanings and dimensions but interrelated in fault characterization—are constructed into a comprehensive state vector. This elevates isolated indicator criteria to a holistic state assessment. By calculating the Mahalanobis distance between this real-time vector and each vector in the abnormal state database, the advantage lies in the fact that this distance not only calculates the proximity between vectors but also considers the inherent fluctuation characteristics and covariance relationships of each parameter under fault conditions, thus more accurately reflecting the probabilistic distance of deviation from known abnormal modes. Finally, the maximum value among all matching degrees is used as the judgment criterion. As long as the current state is highly similar to any known fault mode, even if it is dissimilar to other modes, it is sufficient to determine that a deviation has occurred. This approach is mathematically more rigorous and significantly improves the ability and accuracy of capturing complex faults and atypical initial faults in practice.

[0023] Furthermore, by utilizing the propagation and clustering characteristics of faults in physical space, random false alarms can be distinguished from systemic real faults. The specific physical coordinate information of the state-deviation idlers is correlated and fused with the position mapping database. Spatial clustering analysis of the coordinates of all state-deviation idlers is performed, using geographical proximity as key implicit evidence to determine whether faults originate from the same source. In real industrial scenarios, faults caused by systemic reasons such as belt misalignment, localized overload, and structural deformation inevitably lead to simultaneous or successive state deviations of multiple adjacent idlers within their affected area, naturally forming a deviation cluster in spatial coordinates. In contrast, occasional sensor false alarms and independent damage to individual idlers manifest as isolated points in space. By identifying these clusters, a cognitive leap is achieved from a batch of idler alarms to the potential existence of a common fundamental problem in a specific area. This significantly improves the interpretability, actionability, and engineering value of alarm information, accurately guiding maintenance personnel to focus on the problem area.

[0024] Furthermore, by acquiring the state vector set within a preset identification time period, the analysis dimension is elevated from static state points to dynamic trend lines; cosine similarity is calculated to assess the relative change direction and pattern of each parameter over time. Finally, the maximum value among all similarities is taken as the correlation degree. If the state evolution trend of the tested idler roller is highly synchronized with any confirmed bad neighbor, it indicates that it is very likely to be in the same fault influence field, and thus should be identified as a correlation anomaly. This achieves a leap from identifying obvious fault points that have already occurred to warning of potential risk points that have not yet exceeded the standard but have shown a follow-up deterioration trend, greatly improving the system's predictability of group and progressive faults.

[0025] Furthermore, by comparing the correlation degree of the continuous variable representing the degree of trend synchronization of the idler with a discrete correlation threshold preset based on historical data and operation and maintenance experience, the multi-dimensional and complex time series similarity analysis is converged into a clear binary decision. When the correlation degree exceeds the threshold, it is determined that the idler under test belongs to the same category as the known problem cluster in a statistical sense and should be included in the anomaly set. This not only ensures the consistency and interpretability of the judgment results, but also provides the system with the ability to flexibly adapt to different production line conditions and adjust the early warning sensitivity. Ultimately, it realizes the key leap from intelligent algorithm analysis to executable operation and maintenance instructions. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing in this embodiment; Figure 2 This is a flowchart of the terminal upgrade process in this embodiment; Figure 3 This is a logic diagram of the deviation determination module determining the deviation of the idler roller in this embodiment; Figure 4 This is a logic diagram for identifying abnormal idlers in this embodiment. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please see Figure 1 As shown, this is a schematic diagram of the intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing in this embodiment. This embodiment provides an intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing, including: Several integrated monitoring terminals, which are built into the end caps of each idler roller to be tested, include: The self-powered unit is used to generate its own power based on the rotational mechanical energy of the roller under test; The data acquisition and processing unit is used to acquire temperature and vibration acceleration, and to perform edge calculation processing on the vibration acceleration to obtain vibration intensity and rotor speed; The identification unit is used to store the identification of each idler roller to be tested; The upgrade unit is used to send query requests and receive and execute upgrade instructions; The Internet of Things (IoT) monitoring platform, which is connected to all the aforementioned integrated monitoring terminals, includes: The management module is connected to all the integrated monitoring terminals to obtain the operating status data of each of the tested idlers, including the temperature, vibration intensity and rotor speed, and to establish a position mapping database based on the identity and physical location of each tested idler. In response to the query request, it generates and sends the upgrade command based on the firmware check results. The deviation determination module is used to determine several states that deviate from the idler roller based on the matching degree between the operating status data and the preset abnormal status library. An anomaly identification module is used to identify several abnormal idlers based on the trend correlation degree of the idlers within the deviation cluster. The idler trend correlation degree is determined based on the temporal correlation characteristics of the running state data between each of the idlers under test and each of the state deviation idlers within the deviation cluster. The deviation cluster is determined based on the spatial distribution characteristics of each of the state deviation idlers in the position mapping database. An early warning module is used to generate an abnormality early warning report based on all the abnormal idlers.

[0030] In this embodiment, the early warning module, as the final link of the system decision output, has the core function of integrating, condensing and structuring all the abnormal idler information identified by the aforementioned intelligent analysis process into an abnormal early warning report that can be directly executed by maintenance personnel. This report is not a simple alarm list, but a comprehensive diagnostic document that integrates identity, location, status and correlation. Its content includes at least: (1) Core abnormal list: listing the unique identity of all abnormal idlers that are finally determined; (2) Precise location map: combining the location mapping database, clearly marking the specific physical location of each abnormal idler in the conveyor system, and visually displaying the spatial distribution of the deviation cluster; (3) Detailed status snapshot and trend: providing the current temperature, vibration intensity, speed and other specific values ​​of each abnormal idler, as well as the trend curve of their change within the preset identification time; (4) Correlation description: indicating which abnormal idlers belong to the same deviation cluster, and the strength of the trend correlation between them, thereby indicating the potential root cause of systemic failure; (5) Priority suggestion: automatically generating maintenance priority suggestions based on the severity of abnormal parameters and the size of the associated cluster. This report fundamentally changes the passive situation of traditional monitoring, which is characterized by "alarms being present but difficult to locate and lacking analysis." It achieves a leap from data-driven alarms to knowledge-driven decision-making, providing precise and efficient action guidelines for on-site maintenance.

[0031] The pre-defined abnormal state database is a prior knowledge database stored in the IoT monitoring platform for fault mode matching. It is constructed by collecting, cleaning, extracting features, and clustering historical fault case data, which includes multi-dimensional data such as temperature, vibration intensity, and rotor speed recorded at the time of the fault. Each entry in the database represents a typical fault mode. The abnormal state vector is the core data unit in this database; it is a multi-dimensional vector, with each dimension corresponding to a state monitoring parameter. In this embodiment, these parameters are temperature, vibration intensity, and rotor speed. The specific values ​​of the vector represent the typical values ​​or central trends of these parameters under specific fault modes such as severe bearing wear, complete lubrication failure, and idler roller seizure. Therefore, the abnormal state vector essentially digitizes a specific fault type into a statistically representative standard pattern sample, providing a reliable comparison benchmark for determining deviations in real-time data.

[0032] By constructing a complete intelligent chain from intelligent sensing terminals to cloud-based analysis and decision-making, the mechanical energy of the rotating idler rollers is directly converted into electrical energy at the terminal side, enabling long-term operation of the terminals. Vibration intensity and rotor speed characteristic values ​​are extracted locally in real time through edge computing, greatly reducing the load and latency of wireless data transmission. Each terminal is given a unique identity, laying the foundation for precise management. Remote secure firmware updates are supported, ensuring the sustainable evolution of massive terminal functions. Based on the reliable data stream and identity coordinates provided by the terminals, the IoT monitoring platform performs initial screening by matching real-time data with historical fault patterns, quickly capturing significant anomalies. Then, based on precise location information, spatial clustering of anomaly points filters out isolated false alarms and locates the real problem areas. Finally, in-depth trend analysis is performed within the problem areas, identifying early hidden dangers that have not yet reached the threshold but have shown a trend of coordinated deterioration through time-series correlation analysis. This solves the systemic bottlenecks of traditional monitoring methods in terms of power supply, communication, positioning, and early warning accuracy, achieving a fundamental shift from passive response to proactive prediction of the status of large-scale idler roller clusters. It effectively solves the problems of low monitoring stability and low fault location accuracy caused by long-term power supply difficulties and the difficulty of managing massive nodes.

[0033] Specifically, the self-powered unit includes a stator assembly and a rotor assembly. The rotor assembly rotates with the roller under test and includes a coil winding. The stator assembly includes a permanent magnet and a counterweight, and remains relatively stationary under the action of gravity. When the roller under test rotates, the coil winding cuts the magnetic field lines generated by the permanent magnet to generate an induced electromotive force, converting mechanical energy into electrical energy to power the integrated monitoring terminal.

[0034] In this embodiment, the self-powered unit adopts a gravity pendulum-type self-generating structure, including a stator assembly and a rotor assembly. Based on the principle of electromagnetic induction, the rotor assembly contains coil windings and rotates with the idler roller; the stator assembly consists of permanent magnets and counterweights, remaining relatively stationary under the influence of gravity. When the idler roller rotates, relative motion occurs between the rotor and stator, and the coils cut magnetic field lines, thereby generating an induced electromotive force, realizing the conversion of mechanical energy into electrical energy. Both the stator assembly and the rotor assembly are interference-fitted with bearings to ensure reliable installation and relative movement, thus achieving efficient energy conversion.

[0035] By establishing a relative motion reference frame through a constant gravitational physical field, the mechanical energy of the irregular rotating idler rollers is efficiently converted into electrical energy. The stator assembly remains stationary relative to the ground under the influence of gravity through counterweights, while the rotor assembly rotates synchronously with the idler rollers. A stable relative cutting motion is formed between the two, ensuring that the coil windings can continuously and smoothly cut the magnetic field lines of the permanent magnet at any speed and installation angle, generating a stable induced electromotive force. This provides a continuous, stable, and maintenance-free energy source for the entire integrated monitoring terminal, fundamentally ensuring that raw data such as temperature and vibration acceleration can be continuously and reliably collected. It is the physical foundation for the long-term, automatic, and uninterrupted operation of the entire system's data chain, making large-scale, unattended intelligent monitoring possible.

[0036] Specifically, the vibration intensity is obtained by calculating the root mean square value of the tangential acceleration component within a preset observation period, wherein the tangential acceleration is obtained by performing coordinate transformation and decomposition on the vibration acceleration.

[0037] The preset observation duration refers to the length of time for continuously acquiring tangential acceleration signals to calculate vibration intensity. It depends on the expected minimum operating speed of the idler roller and the system's requirement for feature update frequency, and is usually set between 0.5 and 5 seconds. In this embodiment, it is set to 2 seconds, which can reliably cover tens to hundreds of rotational cycles, effectively filter out instantaneous impact interference, and extract stable feature values ​​that can represent the average vibration energy during that period.

[0038] The original vibration acceleration signal is acquired by a MEMS triaxial accelerometer. According to the coordinate transformation algorithm, the acceleration data in the sensor measurement coordinate system is decomposed into a cylindrical coordinate system with the rotation axis of the idler roller as the reference, so as to accurately separate the tangential acceleration component that reflects the circumferential vibration characteristics of the idler roller. Then, the root mean square value of the tangential acceleration component within the preset observation time is calculated to obtain the vibration intensity that reflects the average vibration energy level.

[0039] The tangential component is precisely separated from the original acceleration signal through coordinate transformation. This component directly reflects abnormal friction and impact within the roller's rotation plane and is highly correlated with major fault modes such as bearing wear and lubrication failure. Subsequently, the root mean square (RMS) value of the tangential acceleration within a preset time period is calculated. As a statistical measure characterizing the power of the vibration signal, the RMS effectively integrates the vibration energy within this time period. It can smooth out instantaneous interference and random noise, and stably reflect the trend of deterioration, providing a sensitive and reliable quantitative input for subsequent fault matching and trend analysis.

[0040] Specifically, the rotor speed is calculated based on the unit conversion of the dominant frequency component of the tangential acceleration component, wherein the dominant frequency component is obtained by performing spectral analysis on the tangential acceleration component and extracting the frequency component formed by the periodic projection of gravitational acceleration.

[0041] By performing spectral analysis on the tangential acceleration component signal, since the acceleration sensor rotates together with the idler roller, the projection of gravitational acceleration on its measurement axis (especially the tangential axis) will exhibit a periodic sinusoidal change with rotation. In the spectrum diagram, the fundamental frequency component generated by the periodic projection of gravity is the dominant frequency component of the tangential acceleration signal. Extracting the frequency value of this dominant frequency component, its physical meaning is the rotational frequency of the idler roller. Finally, through unit conversion, for example, multiplying the frequency (Hz) by 60 to obtain the rotational speed (RPM), the rotor speed can be obtained.

[0042] By utilizing the constant physical reference frame of gravity, the MEMS accelerometer is transformed into a virtual speed sensor that requires no additional hardware. Since the accelerometer rotates synchronously with the idler roller, the projection of gravitational acceleration on its tangential measurement axis changes periodically with the rotation angle. This frequency of change strictly corresponds to the rotation frequency of the idler roller. By performing spectral analysis on the tangential acceleration component, the dominant frequency component with a very high signal-to-noise ratio formed by the gravitational projection can be accurately separated from the complex vibration signal. This achieves the acquisition of key speed information with high reliability and low cost, solely through intelligent mining of existing sensor data, without adding any dedicated speed measuring components. This not only saves hardware costs and space but also avoids speed measurement failures caused by the failure of additional sensors, enabling speed monitoring and vibration monitoring to achieve essential unity and mutual verification at the data source level.

[0043] Specifically, the identification identifier is obtained by scanning the RFID tag of each of the rollers to be tested, and the identification identifier is mapped and bound one-to-one with the preset structured physical location code to establish the location mapping database.

[0044] Through an RFID tag scanning and location binding mechanism, after the idlers are installed, maintenance personnel use handheld RFID scanners to read the built-in electronic tag IDs on-site. Each idler's ID number is identified and mapped to the conveyor segment location number, establishing a one-to-one correspondence between the unique ID of the equipment and its actual installation location. All ID and location-corresponding data are aggregated and integrated to create a location mapping database. The conveyor segment location number adopts a structured coding system, such as "CSD1-Z1-A1". "CSD1" represents the conveyor belt group number, used to distinguish different conveyor belt systems; "Z1" represents the conveyor belt segment number, used to locate the specific segment where the idler is located; and "A1" is the idler number in that segment, divided into several arrangements based on the conveyor's cross-sectional structure, such as A1, A2, A3; B1, B2, B3; C1, C2, C3; D1, D2, D3, etc. This mechanism enables precise positioning and mapping of intelligent idlers within the conveyor system, significantly improving maintenance efficiency and the timeliness of fault tracking.

[0045] By transforming the disordered installation in the physical world into a precisely traceable ordered relationship in the digital world, this addresses the core maintenance pain point of identifiable but unlocatable equipment in industrial settings. In actual installation, smart idlers are often not deployed in a fixed order. While this non-standardized approach increases installation flexibility, it also presents challenges in maintenance, especially when an idler malfunctions. Simply relying on the device ID makes it difficult to quickly pinpoint its physical location within the conveyor system, thus impacting diagnostic and maintenance efficiency. By assigning each idler a unique RFID identifier—its name in the digital system—and a structured physical location code—its physical address—the system establishes an immutable one-to-one correspondence between name and address through on-site scanning and binding. This strongly links abstract status data with specific spatial coordinates, enabling the system to not only report who is malfunctioning when any idler malfunctions but also immediately pinpoint the location of the problem. Data alarms are directly mapped to executable action commands, achieving a closed loop from status monitoring to precise maintenance, significantly improving the management efficiency and fault response speed of large-scale distributed assets.

[0046] Please see Figure 2 As shown, this is a flowchart of the terminal upgrade in this embodiment. In this embodiment, the integrated monitoring terminal wakes up at a preset wake-up cycle and sends a firmware version query request to the IoT monitoring platform at a preset specific wake-up time. In response to the query request, the IoT monitoring platform generates and sends the upgrade instruction to the corresponding integrated monitoring terminal when it determines that there is an available new firmware version.

[0047] The preset wake-up cycle refers to the time interval between two automatic wake-ups of the integrated monitoring terminal from deep sleep and entry into working state. It depends on the application scenario's requirements for real-time status data updates and the overall system power consumption budget, and is typically set between 10 minutes and 24 hours. In this embodiment, it is set to 1 hour, which can capture gradual changes in the idler roller's status while keeping the terminal in a very low-power sleep state most of the time, thus ensuring years of maintenance-free operation with limited self-powered energy.

[0048] Preset specific wake-up times refer to wake-up nodes that are specially defined to perform specific high-level tasks during the terminal's wake-up cycle. This depends on the necessary execution frequency of system maintenance tasks and their priority coordination with regular monitoring tasks. Typically, it is set to wake up every 24th time. That is, after the terminal has completed 24 regular monitoring wake-ups, the upgrade query task is executed on the 25th wake-up. This ensures system maintainability and avoids unnecessary communication overhead and energy waste caused by network queries on every wake-up, thus optimizing resource allocation.

[0049] In this embodiment, the integrated monitoring terminal adopts a low-power design, normally in sleep mode, and wakes up periodically according to a preset wake-up cycle. At a specific wake-up time, the terminal actively sends a query request containing its current firmware version number to the IoT monitoring platform. After receiving the request, the platform's management module queries the latest firmware version corresponding to the terminal model and compares it with the reported version. If an updated and available firmware version exists on the platform, the management module switches to OTA mode, constructs a specific firmware download link for the terminal, and sends this link and upgrade instructions to the terminal. Upon receiving the instruction, the terminal's upgrade unit immediately initiates the upgrade process, sending a connection request to the platform based on the obtained link to establish a secure communication channel. After successful connection, the terminal obtains metadata such as the size of the target firmware file and performs an initial verification with the information sent by the platform to confirm that the file size is consistent, preventing transmission errors. After successful verification, the terminal initializes its internal Flash storage space to prepare for writing new firmware. After successful initialization, it enters the core block download and writing stage. The terminal downloads firmware data packets from the platform in blocks and writes them to the Flash memory one by one. Each time a data block is successfully written, the terminal accumulates the receiving progress. After downloading, it verifies that the total number of bytes received is exactly the same as the target file size sent by the platform to ensure that no data is lost during transmission. After the data integrity verification passes, the terminal calculates the local checksum of the firmware (such as CRC32 or MD5 hash) and compares it with the checksum provided by the platform. This final verification aims to ensure that the downloaded and written firmware data is accurate to the bit level and that no tampering or storage errors have occurred. Only when all the above verification steps are successful is the upgrade process marked as completed. Subsequently, the terminal will automatically restart, load and run the new version of firmware in Flash, thereby completing a secure and reliable remote wireless upgrade. If an error occurs in any of the above stages (such as connection failure, size mismatch, write error, verification failure, etc.), the upgrade process will be terminated, the terminal will record the error log and restore to normal monitoring mode to ensure that the device is not bricked due to an unexpected upgrade.

[0050] By decoupling and optimizing the scheduling of high-frequency, low-latency status monitoring data streams and low-frequency, delayable maintenance command streams in the time dimension, the conventional preset wake-up cycle is dedicated to ensuring the periodic collection and reporting of status data; while special preset specific wake-up times are dedicated to initiating maintenance queries on demand, with a frequency far lower than the monitoring cycle, ensuring the lowest power consumption monitoring mode. Additional resources are consumed only at very few predictable times for health checks, and subsequent high-power upgrade processes are triggered only when the platform confirms that there is new firmware. This achieves the separation of business data and maintenance data and on-demand transmission, achieving a perfect balance of the three key requirements of endurance, real-time monitoring, and software maintainability at the system level. It is the core design supporting the autonomous operation of ultra-large-scale IoT nodes throughout their entire lifecycle.

[0051] Please see Figure 3 As shown, this is a logic diagram for determining whether the deviation determination module deviates from the idler roller in this embodiment. In this embodiment, the deviation determination module includes: The deviation calculation unit is used to construct a real-time state vector based on the temperature, the vibration intensity and the rotor speed, and calculate the Mahalanobis distance between the real-time state vector and each abnormal state vector in the preset abnormal state library to obtain several matching degrees. A deviation determination unit, which is connected to the deviation calculation unit, is used to determine that the idler to be tested is the state deviation idler when the maximum value of all the matching degrees is greater than a preset matching threshold.

[0052] The preset matching threshold is a critical value used to determine whether a real-time state vector is sufficiently similar to a known fault mode. It depends on the Mahalanobis distance distribution statistics of historical fault data, the tolerance for false alarm rate, and the trade-off between the detection sensitivity of different fault modes. It is usually set between 2.0 and 6.0. In this embodiment, it is set to 3.0, which can effectively filter out most of the close-range matches caused by random fluctuations and ensure that only those real-time vectors that significantly deviate from the normal state and are highly pointed to a specific known fault will be judged as state deviations.

[0053] By employing a multidimensional, decorrelation-free, and scale-uniform measurement method, the overall similarity between the current operating state and historical typical fault modes is quantified. Temperature, vibration intensity, and rotor speed—three parameters with different physical meanings and dimensions but interrelated in fault characterization—are constructed into a comprehensive state vector. This elevates isolated indicator criteria to a holistic state assessment. The Mahalanobis distance between this real-time vector and vectors in the abnormal state database is calculated. Its advantage lies in considering not only the proximity of vectors but also the inherent fluctuation characteristics and covariance relationships of each parameter under fault conditions, thus more accurately reflecting the probabilistic distance of deviation from known abnormal modes. Finally, the maximum value among all matching degrees is used as the judgment criterion. If the current state is highly similar to any known fault mode, even if it is dissimilar to other modes, it is sufficient to determine that a deviation has occurred. This approach is mathematically more rigorous and significantly improves the ability and accuracy of capturing complex faults and atypical initial faults in practice.

[0054] Specifically, based on the position mapping database, the physical position coordinates of each state deviation roller are obtained, and spatial clustering analysis is performed on all physical position coordinates to obtain several deviation clusters.

[0055] By querying the location mapping database for the detailed physical location coordinates of the deviating idlers based on their identification identifiers, spatial density clustering analysis is then performed on the set of physical location coordinates of all deviating idlers. Neighboring deviating idlers are grouped into the same set, and each spatially clustered group of deviating idlers is defined as a deviation cluster.

[0056] By leveraging the propagation and clustering characteristics of faults in physical space, this method distinguishes between random false alarms and systemic real faults. It correlates and fuses the specific physical coordinates of the state-deviation idlers with the location mapping database. Spatial clustering analysis of the coordinates of all state-deviation idlers uses geographical proximity as key implicit evidence to determine whether faults originate from the same source. In real industrial scenarios, faults caused by systemic factors such as belt misalignment, localized overload, and structural deformation inevitably lead to simultaneous or successive state deviations of multiple adjacent idlers within their affected area, naturally forming a deviation cluster in spatial coordinates. In contrast, occasional sensor false alarms and independent damage to individual idlers manifest as isolated points in space. By identifying these clusters, a leap from simply reporting a batch of idler alarms to recognizing a common, fundamental problem potentially existing in a specific area is achieved. This significantly enhances the interpretability, actionability, and engineering value of alarm information, accurately guiding maintenance personnel to focus on the problem area.

[0057] Specifically, the real-time state vector of the idler roller to be tested within a preset identification time period is obtained to obtain a set of vectors to be tested; the real-time state vectors of each state deviation from the idler roller within the preset identification time period are obtained to obtain several sets of state deviation vectors; the cosine similarity between the set of vectors to be tested and each set of state deviation vectors is calculated to obtain several trend similarities; and the maximum value among all trend similarities is recorded as the trend correlation degree of the idler roller.

[0058] The preset identification duration refers to the time span of historical state data traced for calculating the trend correlation of the idler roller. It depends on the evolution speed of the fault mode and the system's requirements for the early warning time window. It is usually set between 1 hour and 7 days. In this embodiment, it is set to 24 hours, which can cover multiple complete working cycles of the equipment, including start-up, shutdown, load changes, etc., which is sufficient to filter out short-term random fluctuations, thereby extracting the continuous and directional state evolution trend caused by potential faults.

[0059] By acquiring the state vector set within a preset identification time period, the analysis dimension is elevated from static state points to dynamic trend lines. Cosine similarity is calculated to assess the relative change direction and pattern of each parameter over time. Finally, the maximum value among all similarities is taken as the correlation degree. If the state evolution trend of the tested idler roller is highly synchronized with any confirmed deviating bad neighbor, it indicates that it is highly likely to be in the same fault influence field and should therefore be identified as an anomaly. This achieves a leap from identifying obvious fault points that have already occurred to warning of potential risk points that have not yet exceeded the standard but have shown a tendency to worsen, greatly improving the system's predictability of group and progressive faults.

[0060] Please see Figure 4 As shown, it is the determination logic diagram for identifying abnormal idlers in this embodiment. In this embodiment, when the idler trend correlation degree is greater than the preset correlation threshold, the idler to be tested and the state deviation idler are determined to be abnormal idlers. When the idler trend correlation degree is less than or equal to the preset correlation threshold, the state deviation idler is determined to be an abnormal idler.

[0061] The preset correlation threshold is a critical value used to determine whether the correlation of the idler trend is significant. It depends on the statistical analysis of historical fault data, the tolerance balance between false alarms and false alarms in the specific application scenario, and the experience of operation and maintenance experts. It is usually set between 0.7 and 0.95. In this embodiment, it is set to 0.85 to ensure that only those idlers that show strong synchronicity with the confirmed deviation idlers in terms of state evolution trend will be judged as having abnormal correlation, thus avoiding misjudgments caused by random fluctuations or weak correlation.

[0062] By comparing the correlation degree of the continuous variable representing the degree of trend synchronization of the idler with a discrete correlation threshold preset based on historical data and operation and maintenance experience, the multi-dimensional and complex time series similarity analysis is converged into a clear binary decision. When the correlation degree exceeds the threshold, it is determined that the idler under test belongs to the same category as the known problem cluster in a statistical sense and should be included in the anomaly set. This not only ensures the consistency and interpretability of the judgment results, but also provides the system with the ability to flexibly adapt to different production line conditions and adjust the early warning sensitivity. Ultimately, it realizes the key leap from intelligent algorithm analysis to executable operation and maintenance instructions.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart idler roller IoT monitoring system based on self-powered operation and rotor sensing, characterized in that, include: Several integrated monitoring terminals, which are built into the end caps of each idler roller to be tested, include: The self-powered unit is used to generate its own power based on the rotational mechanical energy of the roller under test; The data acquisition and processing unit is used to acquire temperature and vibration acceleration, and to perform edge calculation processing on the vibration acceleration to obtain vibration intensity and rotor speed; The identification unit is used to store the identification of each idler roller to be tested; The upgrade unit is used to send query requests and receive and execute upgrade instructions; The Internet of Things (IoT) monitoring platform, which is connected to all the aforementioned integrated monitoring terminals, includes: The management module is used to acquire the operating status data of each of the tested idlers, including the temperature, vibration intensity and rotor speed, and to establish a position mapping database based on the identity and physical location of each tested idler. In response to the query request, it generates and sends the upgrade command based on the firmware check results. The deviation determination module is used to determine several states that deviate from the idler roller based on the matching degree between the operating status data and the preset abnormal status library. An anomaly identification module is used to identify several abnormal idlers based on the trend correlation degree of the idlers within the deviation cluster. The idler trend correlation degree is determined based on the temporal correlation characteristics of the running state data between each of the idlers under test and each of the state deviation idlers within the deviation cluster. The deviation cluster is determined based on the spatial distribution characteristics of each of the state deviation idlers in the position mapping database. An early warning module is used to generate an abnormality early warning report based on all the abnormal idlers.

2. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing as described in claim 1, characterized in that, The self-powered unit includes a stator assembly and a rotor assembly. The rotor assembly rotates with the roller under test and includes a coil winding. The stator assembly includes a permanent magnet and a counterweight, and remains relatively stationary under the action of gravity. When the roller under test rotates, the coil winding cuts the magnetic field lines generated by the permanent magnet to generate an induced electromotive force, which converts mechanical energy into electrical energy to power the integrated monitoring terminal.

3. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 2, characterized in that, The vibration intensity is obtained by calculating the root mean square value of the tangential acceleration component within a preset observation period, wherein the tangential acceleration is obtained by performing coordinate transformation and decomposition on the vibration acceleration.

4. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 3, characterized in that, The rotor speed is calculated based on the unit conversion of the dominant frequency component of the tangential acceleration component, wherein the dominant frequency component is obtained by performing spectral analysis on the tangential acceleration component and extracting the frequency component formed by the periodic projection of gravitational acceleration.

5. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 4, characterized in that, The identification identifier is obtained by scanning the RFID tag of each of the rollers to be tested, and the identification identifier is mapped and bound one-to-one with the preset structured physical location code to establish the location mapping database.

6. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 5, characterized in that, The integrated monitoring terminal wakes up periodically according to a preset wake-up cycle and sends a firmware version query request to the IoT monitoring platform at a preset specific wake-up time. In response to the query request, the IoT monitoring platform generates and sends the upgrade instruction to the corresponding integrated monitoring terminal when it determines that a new version of firmware is available.

7. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 6, characterized in that, The deviation determination module includes: The deviation calculation unit is used to construct a real-time state vector based on the temperature, the vibration intensity and the rotor speed, and to calculate several matching degrees based on the real-time state vector and each abnormal state vector in the preset abnormal state library. A deviation determination unit, which is connected to the deviation calculation unit, is used to determine that the idler to be tested is the state deviation idler when the maximum value of all the matching degrees is greater than a preset matching threshold.

8. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 7, characterized in that, Based on the position mapping database, the physical position coordinates of each state deviation roller are obtained, and spatial clustering analysis is performed on all physical position coordinates to obtain several deviation clusters.

9. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 8, characterized in that, The trend correlation degree of the idler roller is calculated based on the trend similarity between the idler roller under test and each of the state deviation idler rollers within the deviation cluster. The trend similarity is calculated based on the test vector set of the idler roller under test and the state deviation vector set of each of the state deviation idler rollers within a preset identification time.

10. The intelligent idler roller IoT monitoring system based on self-powered power supply and rotor sensing according to claim 9, characterized in that, When the trend correlation of the idler roller is greater than a preset correlation threshold, the idler roller to be tested and the idler roller with the state deviation are determined to be abnormal idler rollers. When the trend correlation of the idler roller is less than or equal to the preset correlation threshold, the idler roller with the state deviation is determined to be an abnormal idler roller.

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