Intelligent industrial fault prediction and maintenance method and system based on large model

Abstract

The invention relates to an intelligent industrial fault prediction and maintenance method and system based on a large model. The method comprises the following steps: 1) collecting equipment data; 2) preprocessing and equipment feature extraction; 3) fault prediction driven by a large model; 4) generating a dynamic maintenance plan; and 5) user feedback and model optimization. By using the strong generalization ability of the pre-training large model and the fine tuning of the industrial scene data, the system can realize higher-precision equipment state prediction and dynamic maintenance optimization, so that the equipment operation risk is reduced, and the industrial production efficiency is improved. By using the strong generalization ability of the pre-training large model and the fine tuning of the industrial scene data, the system can realize higher-precision equipment state prediction and dynamic maintenance optimization, so that the equipment operation risk is reduced, and the industrial production efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of industrial automation and artificial intelligence technology, and specifically is a large-model-based intelligent industrial fault prediction and maintenance method and system, which is widely applicable to equipment operation monitoring and maintenance in manufacturing, energy, transportation and other industrial scenarios. Background Art

[0002] Currently, fault prediction and maintenance of industrial equipment mainly rely on traditional algorithms based on data input manually or acquired by sensors, such as rule-based systems or simple statistical analysis. These methods have the following problems: 1. Low prediction accuracy: Traditional methods have difficulty capturing nonlinear relationships in complex devices, and the prediction results are not accurate enough.

[0003] 2. Strong data dependence: Existing methods require a large amount of manually labeled data and experience support, which is costly.

[0004] 3. Fixed maintenance cycle: Most equipment still uses a fixed-cycle maintenance mode, which cannot achieve dynamic optimization, resulting in waste of maintenance resources or increased equipment risks.

[0005] With the development of deep learning and artificial intelligence technology, researchers have tried to introduce deep neural networks for fault prediction. However, these methods are still limited in their performance on small sample data, and the models are difficult to generalize to different equipment scenarios.

[0006] Therefore, an intelligent industrial fault prediction and maintenance method and system based on a large model is designed to overcome the above problems. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an intelligent industrial fault prediction and maintenance method and system based on a large model. By utilizing the strong generalization ability of the pre-trained large model and the fine tuning of industrial scene data, the system can achieve higher-precision equipment status prediction and dynamic maintenance optimization, thereby reducing equipment operation risks and improving industrial production efficiency.

[0008] The present invention is implemented by the following technical solution: an intelligent industrial fault prediction and maintenance method based on a large model, the method comprising the following steps: 1) Equipment data collection; 2) Preprocessing and device feature extraction; 3) Fault prediction driven by large models; 4) Dynamic maintenance plan generation; 5) User feedback and model optimization.

[0009] Preferably, in step 1, the device data is collected through sensors arranged on the device to collect the operating status of the device in real time, and the status is transmitted to the Internet of Things gateway manually or automatically and then transmitted to the system.

[0010] Preferably, the specific method of the step 2 reprocessing and equipment feature extraction is: the data collected by the sensor is first filtered out of noise and interference, and then the data is standardized so that different types of data are within a unified range. Finally, according to the logic of the system, features are extracted, such as frequency changes and numerical fluctuations, and stored in the database after analysis.

[0011] Preferably, the specific method of fault prediction driven by the big model in step 3 is: introducing a pre-trained big model to perform multimodal fusion analysis on the equipment data, constructing equipment status representation, fine-tuning the big model in the industrial scenario, using annotated data sets to optimize its recognition ability for specific equipment and fault modes, using real-time input data to predict the equipment health status score, and giving possible fault types and occurrence probabilities.

[0012] Preferably, the specific method for generating the dynamic maintenance plan in step 4 is: based on the fault prediction results, combined with the equipment operation plan and maintenance resource constraints, generate the optimal maintenance strategy, dynamically adjust the maintenance plan, and determine the specific maintenance time window, required personnel and spare parts.

[0013] Preferably, the method for pre-training the large model is: 1) First establish a large model foundation, and then input various structural faults and solutions to the faults into the large model; 2) After a large amount of fault data is fed to the big data for training, the solution is fine-tuned, such as continuing the training after manual input of problems that cannot be solved; 3) When the training accuracy reaches more than 90%, it can be used as the basis for application.

[0014] A large-model-based intelligent industrial fault prediction and maintenance system, the system comprising: Data collector: This collector includes an acceleration sensor, a temperature sensor, a current sensor and an acoustic wave pickup, and collects vibration, temperature, current and acoustic data; Data preprocessing and feature extraction module: The data collected by the device's sensors are first filtered out of noise and interference, and then the data is standardized so that different types of data are within a unified range. Finally, according to the system logic, frequency changes and value fluctuations are extracted, analyzed and stored in the database; Big model-driven fault prediction module: Through the data in the big model database, the above data is subjected to multimodal fusion analysis to construct equipment status representation, fine-tune the big model in industrial scenarios, use annotated data sets to optimize its recognition ability for specific equipment and fault modes, and use real-time input data to predict equipment health status scores and give possible fault types and probabilities. Dynamic maintenance plan generation module: Based on the fault prediction results, combined with the equipment operation plan and maintenance resource constraints, it generates the optimal maintenance strategy, dynamically adjusts the maintenance plan, and determines the specific maintenance time window, required personnel and spare parts; User feedback and model optimization module: Display prediction results and maintenance suggestions through the user interface, obtain user feedback on prediction accuracy and maintenance effects, use feedback data to continuously learn and optimize the large model, and improve the long-term performance of the system.

[0015] The beneficial effects of the present invention are as follows: 1. High-precision fault prediction: Improve the accuracy of fault prediction through the strong generalization ability of large models and multimodal data fusion.

[0016] 2. Dynamic maintenance optimization: Generate dynamic maintenance strategies based on real-time data to effectively reduce equipment downtime.

[0017] 3. Cross-device scenario adaptation: Through transfer learning and adaptive optimization, the system can adapt to the fault prediction needs of various types of equipment.

[0018] 4. Significant economic benefits: It reduces unnecessary maintenance investment, improves equipment utilization and production efficiency, and reduces operating costs.

[0019] Through the present invention, the intelligent operation and maintenance level of industrial equipment is greatly improved, providing reliable technical support for realizing intelligent manufacturing and industrial digital transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to more clearly understand the objectives, technical solutions and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0022] In the description of the present invention, it is necessary to understand that the orientation or position relationship indicated by the terms such as "up", "down", "left", "right", "inside", "outside", "horizontal" and "vertical" is based on the orientation or position relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention, rather than indicating or implying that the device or original referred to must have a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0023] The present invention will be described in detail below with reference to the accompanying drawings: Figure 1 As shown, a large model-based intelligent industrial fault prediction and maintenance method comprises the following steps: 1) Equipment data collection; 2) Preprocessing and device feature extraction; 3) Fault prediction driven by large models; 4) Dynamic maintenance plan generation; 5) User feedback and model optimization.

[0024] In step 1, the device data is collected through sensors arranged on the device to collect the operating status of the device in real time. The status is transmitted to the IoT gateway manually or automatically and then transmitted to the system.

[0025] The sensors may include accelerometers, temperature sensors, current sensors and acoustic wave pickups, which collect vibration, temperature, current and acoustic data of industrial equipment in real time, and then transmit them to the system through the IoT gateway to ensure low latency and high reliability.

[0026] The specific method of the step 2 heavy industry preprocessing and equipment feature extraction is: the data collected by the sensor is first filtered out of noise and interference, such as wavelet transform, moving average, etc., and then the data is standardized so that different types of data are within a unified range. Finally, according to the logic of the system, some useful features are extracted, such as frequency changes, numerical fluctuations, and some more complex characteristics, which are analyzed and stored in the database.

[0027] The specific method of fault prediction driven by the big model in step 3 is: introduce a pre-trained big model to perform multimodal fusion analysis on the equipment data, build equipment status representation, fine-tune the big model in the industrial scenario, use annotated data sets to optimize its recognition ability for specific equipment and fault modes, use real-time input data to predict the equipment health status score, and give possible fault types and occurrence probabilities.

[0028] Preferably, the specific method for generating the dynamic maintenance plan in step 4 is: based on the fault prediction results, combined with the equipment operation plan and maintenance resource constraints, generate the optimal maintenance strategy, dynamically adjust the maintenance plan, and determine the specific maintenance time window, required personnel and spare parts.

[0029] The method for pre-training the large model is: 1) First establish a large model foundation, and then input various structural faults and solutions to the faults into the large model; 2) After a large amount of fault data is fed to the big data for training, the solution is fine-tuned, such as continuing the training after manual input of problems that cannot be solved; 3) When the training accuracy reaches more than 90%, it can be used as the basis for application.

[0030] A large-model-based intelligent industrial fault prediction and maintenance system, the system comprising: Data collector: This collector includes an acceleration sensor, a temperature sensor, a current sensor and an acoustic wave pickup, and collects vibration, temperature, current and acoustic data; Data preprocessing and feature extraction module: The data collected by the device's sensors are first filtered out of noise and interference, and then the data is standardized so that different types of data are within a unified range. Finally, according to the system logic, frequency changes and value fluctuations are extracted, analyzed and stored in the database; Big model-driven fault prediction module: Through the data in the big model database, the above data is subjected to multimodal fusion analysis to construct equipment status representation, fine-tune the big model in industrial scenarios, use annotated data sets to optimize its recognition ability for specific equipment and fault modes, and use real-time input data to predict equipment health status scores and give possible fault types and probabilities. Dynamic maintenance plan generation module: Based on the fault prediction results, combined with the equipment operation plan and maintenance resource constraints, it generates the optimal maintenance strategy, dynamically adjusts the maintenance plan, and determines the specific maintenance time window, required personnel and spare parts; User feedback and model optimization module: Display prediction results and maintenance suggestions through the user interface, obtain user feedback on prediction accuracy and maintenance effects, use feedback data to continuously learn and optimize the large model, and improve the long-term performance of the system.

[0031] Anyone familiar with the technology may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A large-model-based intelligent industrial fault prediction and maintenance method, characterized in that: The method comprises the following steps: 1) Equipment data collection; 2) Preprocessing and device feature extraction; 3) Fault prediction driven by large models; 4) Dynamic maintenance plan generation; 5) User feedback and model optimization.

2. The large model-based intelligent industrial fault prediction and maintenance method according to claim 1 is characterized in that: In step 1, the device data is collected through sensors arranged on the device to collect the operating status of the device in real time. The status is transmitted to the IoT gateway manually or automatically and then transmitted to the system.

3. The large model-based intelligent industrial fault prediction and maintenance method according to claim 1 is characterized in that: The specific method of the step 2 heavy industry preprocessing and equipment feature extraction is: the data collected by the sensor is first filtered out of noise and interference, and then the data is standardized so that different types of data are within a unified range. Finally, according to the logic of the system, features are extracted, such as frequency changes and numerical fluctuations, and stored in the database after analysis.

4. The large model-based intelligent industrial fault prediction and maintenance method according to claim 1 is characterized in that: The specific method of fault prediction driven by the big model in step 3 is: introduce a pre-trained big model to perform multimodal fusion analysis on the equipment data, build equipment status representation, fine-tune the big model in the industrial scenario, use annotated data sets to optimize its recognition ability for specific equipment and fault modes, use real-time input data to predict the equipment health status score, and give possible fault types and occurrence probabilities.

5. The large model-based intelligent industrial fault prediction and maintenance method according to claim 1 is characterized in that: The specific method for generating the dynamic maintenance plan in step 4 is: based on the fault prediction results, combined with the equipment operation plan and maintenance resource constraints, generate the optimal maintenance strategy, dynamically adjust the maintenance plan, and determine the specific maintenance time window, required personnel and spare parts.

6. The large model-based intelligent industrial fault prediction and maintenance method according to claim 4 is characterized in that: The method for pre-training the large model is: 1) First establish a large model foundation, and then input various structural faults and solutions to the faults into the large model; 2) After a large amount of fault data is fed to the big data for training, the solution is fine-tuned, such as continuing the training after manual input of problems that cannot be solved; 3) When the training accuracy reaches more than 90%, it can be used as the basis for application.

7. An intelligent industrial fault prediction and maintenance system supported by a large model, characterized in that: The system comprises: Data collector: This collector includes an acceleration sensor, a temperature sensor, a current sensor and an acoustic wave pickup, and collects vibration, temperature, current and acoustic data; Data preprocessing and feature extraction module: The data collected by the device's sensors are first filtered out of noise and interference, and then the data is standardized so that different types of data are within a unified range. Finally, according to the system logic, frequency changes and value fluctuations are extracted, analyzed and stored in the database; Big model-driven fault prediction module: Through the data in the big model database, the above data is subjected to multimodal fusion analysis to construct equipment status representation, fine-tune the big model in industrial scenarios, use annotated data sets to optimize its recognition ability for specific equipment and fault modes, and use real-time input data to predict equipment health status scores and give possible fault types and probabilities. Dynamic maintenance plan generation module: Based on the fault prediction results, combined with the equipment operation plan and maintenance resource constraints, it generates the optimal maintenance strategy, dynamically adjusts the maintenance plan, and determines the specific maintenance time window, required personnel and spare parts; User feedback and model optimization module: Display prediction results and maintenance suggestions through the user interface, obtain user feedback on prediction accuracy and maintenance effects, use feedback data to continuously learn and optimize the large model, and improve the long-term performance of the system.

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