Equipment maintenance and fault monitoring method

Abstract

The invention relates to the technical field of sensor fault diagnosis, and particularly discloses an equipment maintenance and fault monitoring method, which comprises the steps of signal data acquisition: acquiring real-time data from a plurality of sensors, including sensor signals, timestamps and sensor state information; data preprocessing: preprocessing the collected original data; multi-sensor fusion: fusing redundant data of a plurality of sensors into a reliable estimated value by adopting a multi-sensor fusion technology; anomaly detection: carrying out anomaly detection on the fused data by using a one-field anomaly detection algorithm; fault diagnosis: when an anomaly is detected, the system diagnoses a sensor fault according to the mode and severity of the anomaly, and outputs a fault type and confidence; and alarm and feedback: the system gives an alarm according to the diagnosis result and provides corresponding feedback information so as to facilitate maintenance and repair. The method has the advantages of improving fault detection accuracy and reliability, realizing real-time diagnosis and adapting to complex and changeable environments.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor fault diagnosis, and in particular to a method for monitoring equipment maintenance faults. Background Art

[0002] Traditional sensor fault detection methods typically rely on data from a single sensor, which has numerous limitations. First, data from a single sensor is easily affected by environmental noise, resulting in inaccurate detection results. Second, when a single sensor fails, the reliability of the entire system is severely impacted, potentially leading to erroneous fault diagnosis results. Furthermore, single-sensor methods fail to fully utilize redundant information across multiple sensors, reducing the accuracy and robustness of fault detection.

[0003] Existing technologies lack a method for effectively integrating multi-sensor information. Multi-sensor systems offer the advantage of information redundancy, which can improve the reliability of fault detection, but effectively integrating this information remains a challenge. Furthermore, existing technologies lack effective methods for diagnosing sensor failures in real time. In scenarios such as industrial production, timely detection and diagnosis of sensor failures are crucial for ensuring system safety and production efficiency, but existing technologies often fail to meet real-time requirements.

[0004] Furthermore, existing fault diagnosis systems typically use fixed detection algorithms and thresholds, making them difficult to adapt to complex and changing operating environments and varying system operating states. This lack of flexibility can lead to false positives or false negatives, compromising the accuracy of fault diagnosis.

[0005] Therefore, it is necessary to propose an improvement to overcome the defects of the prior art. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems in the prior art and provide a method for monitoring equipment maintenance faults to solve the accuracy and reliability problems of sensor fault detection.

[0007] The technical solution of the present invention is: A method for equipment maintenance and fault monitoring comprises the following steps: Step 1: Signal data acquisition: Collect real-time data from multiple sensors, including sensor signals, timestamps, and sensor status information; Step 2: Data preprocessing: preprocess the collected raw data; Step 3: Multi-sensor fusion: Multi-sensor fusion technology is used to fuse the redundant data of multiple sensors into a reliable estimate; Step 4: Anomaly detection: Use an anomaly detection algorithm to detect anomalies on the fused data; Step 5: Fault Diagnosis: When an anomaly is detected, the system diagnoses sensor faults based on the anomaly pattern and severity, and outputs the fault type and confidence level. Step 6: Alarm and Feedback: The system issues an alarm based on the diagnosis result and provides corresponding feedback information for maintenance and repair.

[0008] As a preferred technical solution, the sensors are not limited to temperature sensors, pressure sensors, and flow sensors.

[0009] As a preferred technical solution, the data preprocessing methods include denoising, filtering, and data cleaning.

[0010] Compared with the prior art, the beneficial effects of the present invention are: The device maintenance and fault monitoring method of the present invention includes steps such as signal data acquisition, data preprocessing, multi-sensor fusion, anomaly detection, fault diagnosis, and alarm and feedback. Through multi-sensor fusion technology and anomaly detection algorithms, it realizes real-time and accurate diagnosis of sensor faults, and has the advantages of improving the accuracy and reliability of fault detection, realizing real-time diagnosis, and adapting to complex and changeable environments. Brief Description of the Drawings

[0011] Figure 1 is a flowchart of the device maintenance and fault monitoring method of the present invention. Detailed Embodiments

[0012] In order to make the technical means, technical features, invention purposes, and technical effects achieved by the present invention easy to understand, the present invention is further described below with reference to specific drawings.

[0013] As Figure 1 shown, it is a flowchart of the device maintenance and fault monitoring method of the present invention.

[0014] Signal data acquisition: Real-time data is collected from multiple sensors, including sensor signals, timestamps, and sensor status information. The types of sensors can be various, such as temperature sensors, pressure sensors, flow sensors, etc. Data preprocessing: The collected raw data is preprocessed, such as denoising, filtering, data cleaning, etc., to improve the quality and reliability of the data. Kalman filtering or other suitable filtering algorithms can be used.

[0015] Multi-sensor fusion: Multi-sensor fusion technology, such as Kalman filtering or other suitable fusion algorithms, is used to fuse the redundant data of multiple sensors into a reliable estimated value, which can reduce the impact of single sensor faults.

[0016] Anomaly detection uses anomaly detection algorithms, such as statistical-based methods and machine learning-based methods, to perform anomaly detection on the fused data. The parameters of the anomaly detection algorithm can be dynamically adjusted according to historical data and the system operating status.

[0017] Fault diagnosis: When an anomaly is detected, the system diagnoses the sensor fault according to the pattern and severity of the anomaly and outputs the fault type and confidence level. Alarm and feedback: The system issues an alarm according to the diagnosis result and provides corresponding feedback information for maintenance and repair.

[0018] The technical features included in this application are signal data acquisition, data preprocessing, multi-sensor fusion, anomaly detection, fault diagnosis, alarm and feedback. These technical features play an important role in solving the problem that the data of a single sensor in a real-time fault diagnosis system is vulnerable to noise and faults. Signal data acquisition ensures that the system can obtain data from multiple sensors. Data preprocessing improves the quality and reliability of the data. Multi-sensor fusion reduces the impact of a single sensor fault by fusing data from multiple sensors. Anomaly detection improves the accuracy of anomaly detection by dynamically adjusting parameters. Fault diagnosis provides accurate fault types and confidence levels by analyzing the pattern and severity of anomalies. Alarm and feedback ensure that the system can issue an alarm in a timely manner and provide feedback information for maintenance and repair. Through the above solution, this application solves the technical problem that the data of a single sensor in a real-time fault diagnosis system is vulnerable to noise and faults, and improves the accuracy and reliability of fault diagnosis.

[0019] In the signal data acquisition step, the sensor types can include but are not limited to temperature sensors, pressure sensors, flow sensors, etc. These sensors can be connected to the data acquisition system by wired or wireless means. In the data preprocessing step, denoising can adopt methods such as Kalman filtering, mean filtering, median filtering, etc. The selection of the filtering algorithm can be adjusted according to the actual application scenario and data characteristics. In the multi-sensor fusion step, in addition to Kalman filtering, methods such as particle filtering and Bayesian estimation can also be used for data fusion to improve the reliability of the fusion result. In the anomaly detection step, the statistical-based method can include the 3-sigma rule, and the machine learning-based methods can include support vector machines, isolation forests, autoencoders, etc. The parameters of the algorithm can be optimized through historical data training and system self-learning. In the fault diagnosis step, according to the results of anomaly detection, methods such as pattern recognition and fault tree analysis can be used to diagnose the fault type and severity. In the alarm and feedback step, the system can issue an alarm in various ways such as sound, light, SMS, email, etc. and provide detailed fault information and maintenance suggestions.

[0020] Through multi-sensor fusion and anomaly detection technologies, this application effectively solves the problem that single-sensor data is vulnerable to noise and faults. Compared with the prior art, this application can collect and process various sensor data in real time, improve the quality and reliability of data through data preprocessing and multi-sensor fusion, improve the accuracy and timeliness of fault detection through anomaly detection and fault diagnosis, and finally ensure that the system can respond and handle faults in a timely manner through an alarm and feedback mechanism, significantly improving the stability and reliability of the system.

[0021] In summary, the above are only the preferred embodiments of the present invention and are not used to limit the scope of implementation of the present invention. That is, all equivalent changes and modifications made to the content within the scope of the patent application of the present invention shall fall within the technical scope of the present invention.

Claims

1. A device maintenance and fault monitoring method, characterized in that Including the following steps: Step 1: Signal data acquisition: Collect real-time data from multiple sensors, including sensor signals, timestamps, and sensor status information; Step 2: Data preprocessing: Preprocess the collected raw data; Step 3: Multi-sensor fusion: Adopt multi-sensor fusion technology to fuse redundant data from multiple sensors into a reliable estimated value; Step 4: Anomaly detection: Use an anomaly detection algorithm to detect anomalies in the fused data; Step 5: Fault diagnosis: When an anomaly is detected, the system will diagnose the sensor fault according to the anomaly pattern and severity, and output the fault type and confidence level; Step 6: Alarm and feedback: The system will issue an alarm according to the diagnosis result and provide corresponding feedback information for maintenance and repair.

2. The device maintenance and fault monitoring method according to claim 1, wherein The sensors are not limited to temperature sensors, pressure sensors, and flow sensors.

3. The equipment maintenance and fault monitoring method according to claim 1, characterized in that, The data preprocessing methods include denoising, filtering, and data cleaning.

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