Intelligent equipment fault diagnosis method based on multi-modal fusion

Abstract

The invention discloses an intelligent equipment fault diagnosis method based on multi-modal fusion, and the method achieves the quick and accurate diagnosis of an intelligent equipment fault through integrating various sensor data such as sound, images, vibration and temperature, and carrying out the automatic analysis through a deep learning model. The invention aims to improve the accuracy and efficiency of fault diagnosis, reduce manual intervention, reduce the maintenance cost and improve the reliability of equipment operation. The technology is suitable for the fields of industrial automation, intelligent manufacturing and the like, and an efficient solution is provided for equipment maintenance and management.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent device fault diagnosis, and in particular relates to an intelligent device fault diagnosis method based on multi-modal fusion. Background Art

[0002] In the industrial and manufacturing fields, fault diagnosis of intelligent equipment is a key technology to ensure production continuity and equipment health. Fault diagnosis usually involves monitoring the operating status of equipment, including the detection of multiple physical quantities such as sound, vibration, temperature, etc., to identify abnormal indicators that may lead to performance degradation or failure.

[0003] Currently, fault diagnosis technology mainly relies on single-modal data analysis, such as using only vibration or sound data for fault detection. This approach may not accurately reflect the overall status of the equipment, because a single data source may not be sufficient to capture all types of faults. In addition, existing fault diagnosis methods often lack sufficient automation and intelligence when processing and analyzing data, and require a lot of manual intervention.

[0004] The present invention aims to solve the problem of low accuracy and low efficiency of fault diagnosis of intelligent devices in the prior art. In particular, it seeks to improve the accuracy and efficiency of fault diagnosis by integrating data from multiple modalities and automatically analyzing these data using deep learning technology. The method aims to reduce manual intervention and achieve faster and more reliable fault detection. Summary of the invention

[0005] In order to solve the problems of the prior art, an embodiment of the present invention provides a method for intelligent device fault diagnosis based on multimodal fusion. The technical solution is as follows:

[0006] On the one hand, a method for intelligent device fault diagnosis based on multimodal fusion is provided, comprising the following steps:

[0007] Collect the sound, image, vibration and temperature data generated by the equipment during operation;

[0008] Preprocessing the data, including denoising, feature extraction and normalization;

[0009] Analyze the preprocessed data using deep learning models, including convolutional neural networks and recurrent neural networks;

[0010] The characteristics of each modal data are integrated through the modal fusion strategy to improve the accuracy of fault diagnosis; and the machine learning classification algorithm is used to determine the fault type of the fused data.

[0011] Furthermore, the sound data is collected by a microphone array, which captures sound signals in a frequency range from 20 Hz to 20 kHz.

[0012] Furthermore, the image data is collected by a high-resolution camera having a resolution of at least 20 million pixels and the capability of infrared imaging.

[0013] Furthermore, the vibration data is collected by an accelerometer, and the accelerometer detects vibration signals within a frequency range of 0.1 to 500 Hz.

[0014] Furthermore, the temperature data is collected by a temperature sensor, and the temperature sensor measures the temperature in the range of -50°C to 150°C.

[0015] Furthermore, the data preprocessing includes extracting spectral features of the sound data using fast Fourier transform and performing time-frequency analysis using wavelet transform.

[0016] Furthermore, the image processing includes using edge detection and pattern recognition techniques to identify abnormal signs in image data, and applying deep learning algorithms to perform image segmentation and feature classification.

[0017] Furthermore, the training of the deep learning model includes using a labeled fault data set for supervised learning and combining transfer learning technology to improve the generalization ability of the model.

[0018] Furthermore, the modality fusion strategy includes weighted fusion and decision-level fusion techniques, as well as feature-level fusion using a multi-layer perceptron.

[0019] Furthermore, the machine learning classification algorithm includes using a support vector machine to determine the fault type, and optimizing the parameter settings of the support vector machine in combination with a genetic algorithm.

[0020] The technical solution provided by the embodiment of the present invention has the following beneficial effects:

[0021] Improved diagnostic accuracy: By integrating multimodal data, the present invention can more comprehensively capture the equipment status, thereby improving the accuracy of fault detection.

[0022] Improve diagnostic efficiency: By using deep learning technology to automatically analyze data, the present invention reduces manual intervention and speeds up fault diagnosis.

[0023] Reduced maintenance costs: Accurate and fast fault diagnosis helps to fix problems in a timely manner, avoiding long downtime and thus reducing maintenance costs.

[0024] Extending device life: By detecting and resolving faults early, the present invention helps extend the life of smart devices.

[0025] Enhance system reliability: The fault diagnosis method provided by the present invention can improve the stability and reliability of the production line, which is crucial to ensuring production safety and continuity.

[0026] Easy to integrate and expand: The method of the present invention is easy to integrate into an existing monitoring system and can be expanded and customized according to different application environments.

[0027] Support real-time monitoring: The present invention can realize real-time fault monitoring and provide instant feedback and maintenance suggestions to operators.

[0028] Adaptive learning capability: The fault diagnosis system of the present invention can optimize itself by continuously learning new fault cases, thereby improving future diagnostic efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 It is a flowchart of a method for intelligent device fault diagnosis based on multimodal fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] The present invention provides a method for intelligent device fault diagnosis based on multi-modal fusion, see Figure 1 , the following is a detailed description of each method step of the present invention:

[0033] 1. Sound data collection

[0034] Use microphone arrays to capture sound signals generated when the device is running.

[0035] The microphone array covers a frequency range of 20Hz to 20kHz to ensure that all relevant sound frequencies are captured.

[0036] 2. Image data acquisition

[0037] A high-resolution camera is used to capture image data of the device.

[0038] The camera has a resolution of at least 20 megapixels to capture clear images and has infrared imaging capabilities to work in different lighting conditions.

[0039] 3. Vibration data collection

[0040] Accelerometers are used to detect vibration signals of equipment.

[0041] Accelerometers are capable of detecting vibrations in the frequency range of 0.1 to 500 Hz, covering the vibration frequencies of most equipment during operation.

[0042] 4. Temperature data collection

[0043] Temperature sensors are used to monitor the temperature of key parts of equipment.

[0044] The sensor is capable of operating in extreme temperature conditions, with a measurement range of -50°C to 150°C.

[0045] 5. Data preprocessing

[0046] The collected data is subjected to denoising, feature extraction and normalization.

[0047] Fast Fourier Transform (FFT) is used to extract spectral features of sound data.

[0048] Time-frequency analysis is performed using wavelet transform to better understand the temporal changes of sound signals.

[0049] 6. Image processing

[0050] Apply edge detection and pattern recognition techniques to identify abnormal signs in image data.

[0051] Use deep learning algorithms for image segmentation and feature classification to identify potential issues on equipment surfaces.

[0052] 7. Deep learning model training

[0053] The deep learning model is trained using the labeled fault dataset.

[0054] Combined with transfer learning technology, the generalization ability of the model is improved so that it can adapt to fault diagnosis of different equipment.

[0055] 8. Modal Fusion Strategy

[0056] Weighted fusion and decision-level fusion techniques are adopted, and multi-layer perceptron is used for feature-level fusion.

[0057] Through this fusion strategy, the system can integrate the characteristics of each modal data and improve the accuracy of fault diagnosis.

[0058] Fault type determination

[0059] 9. Use support vector machine (SVM) to determine the fault type.

[0060] The genetic algorithm is combined to optimize the parameter settings of SVM to improve the accuracy of classification.

[0061] The specific implementation of the present invention details how to realize the diagnosis of intelligent device faults by integrating multiple sensor data and applying deep learning models. This technical solution solves the problems of low accuracy and low diagnostic efficiency of intelligent device fault diagnosis in the prior art, and improves the accuracy and efficiency of fault diagnosis.

[0062] Without departing from the gist and scope of the present invention, other technical solutions or detail changes that may be adopted by the present invention include but are not limited to the following:

[0063] Data collection variants: In addition to using microphone arrays, high-resolution cameras, accelerometers, and temperature sensors, other sensing technologies such as laser scanning and ultrasonic detection can also be used to collect device status data.

[0064] Data preprocessing deformation: More advanced signal processing techniques, such as adaptive filters and machine learning algorithms, can be used to remove noise and extract features more effectively.

[0065] Deep learning model deformation: In addition to convolutional neural networks and recurrent neural networks, other deep learning architectures such as generative adversarial networks (GANs) and variational autoencoders (VAEs) can also be used to process and analyze data.

[0066] Modal fusion strategy variation: Other fusion techniques such as feature-level fusion, decision tree fusion, or rule-based fusion methods can be explored to improve the accuracy and reliability of fault diagnosis.

[0067] Fault type determination variation: In addition to support vector machines, other classification algorithms such as decision trees, random forests, neural networks, etc. can also be used to determine the fault type.

[0068] System feedback transformation: The system can be integrated with mobile applications or remote monitoring platforms so that users can receive real-time fault diagnosis information and maintenance suggestions at any location.

[0069] Adaptive learning deformation: The system can be designed to continuously update its model parameters during the diagnosis process to adapt to new failure modes and changing equipment conditions.

[0070] User interface transformation: A more intuitive user interface can be developed so that non-professionals can easily understand and operate the fault diagnosis system.

[0071] These variations and technical details enable the present invention to maintain flexibility and adaptability in different application environments and conditions, while ensuring the accuracy and efficiency of fault diagnosis. These variations also provide broad space for further research and development of the present invention.

[0072] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent device fault diagnosis based on multimodal fusion, characterized in that: The following steps are involved: Collect the sound, image, vibration and temperature data generated by the equipment during operation; Preprocessing the data, including denoising, feature extraction and normalization; Analyze the preprocessed data using deep learning models, including convolutional neural networks and recurrent neural networks; The characteristics of each modal data are integrated through the modal fusion strategy to improve the accuracy of fault diagnosis; and the machine learning classification algorithm is used to determine the fault type of the fused data.

2. The diagnostic method according to claim 1, characterized in that The sound data is collected by a microphone array, which captures sound signals in a frequency range from 20 Hz to 20 kHz.

3. The diagnostic method according to claim 2, characterized in that The image data is collected by a high-resolution camera having a resolution of at least 20 million pixels and the capability of performing infrared imaging.

4. The diagnostic method according to claim 3, characterized in that: The vibration data is collected by an accelerometer, which detects vibration signals within a frequency range of 0.1 to 500 Hz.

5. The diagnostic method according to claim 4, characterized in that: The temperature data is collected by a temperature sensor, and the temperature sensor measures the temperature in the range of -50°C to 150°C.

6. The diagnostic method according to claim 1, characterized in that: The data preprocessing includes extracting spectrum features of the sound data using fast Fourier transform and performing time-frequency analysis using wavelet transform.

7. The diagnostic method according to claim 6, characterized in that: The image processing includes using edge detection and pattern recognition techniques to identify abnormal signs in image data, and applying deep learning algorithms to perform image segmentation and feature classification.

8. The diagnostic method according to claim 1, characterized in that: The training of the deep learning model includes supervised learning using a labeled fault dataset and combining transfer learning technology to improve the generalization ability of the model.

9. The diagnostic method according to claim 1, characterized in that: The modality fusion strategy includes weighted fusion and decision-level fusion techniques, as well as feature-level fusion using a multi-layer perceptron.

10. The diagnostic method according to claim 1, characterized in that The machine learning classification algorithm includes using a support vector machine to determine the fault type and optimizing the parameter settings of the support vector machine in combination with a genetic algorithm.

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