Industrial Equipment Failure Prediction Using Selective Deep Models
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Solution Overview
Problem
Conventional methods for predicting failures in industrial equipment are inadequate as they detect failures only after they have occurred, leading to product defects and reduced productivity, and fail to account for the varying operating characteristics and environments of different machine tools.
Innovation Solution
A failure prediction device and method using multiple deep learning models, including LSTM, transformer, and TCN models, to collect and analyze operational data from sensors, preprocess it using autoencoders, and select the best model for predicting future operational data to anticipate equipment failures before they happen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional failure detection methods are used, then failures can be detected after they occur, but productivity decreases due to product defects and inability to prevent failures
Solution Approach 1:
The system performs preliminary failure prediction by analyzing operational data trends before actual failures occur. Multiple deep learning models predict future operational states and identify potential failures in advance, allowing preventive maintenance actions to be taken before productivity is impacted by actual failures or defective products.
2Device complexity
If a single deep learning model is used, then the system is simpler, but prediction accuracy decreases for various types of industrial equipment with different operating characteristics
Solution Approach 1:
The system implements a universal multi-model framework where multiple deep learning models (LSTM, Transformer, TCN) are integrated to handle various types of industrial equipment with different operating characteristics. Each model type captures different temporal patterns, and the ensemble approach provides universal applicability across diverse equipment while maintaining high prediction accuracy.
Solution Approach 2:
The system merges multiple deep learning models (LSTM for sequential dependencies, Transformer for attention-based long-range dependencies, TCN for temporal convolution) into a unified prediction framework. By combining the strengths of different model architectures, the system achieves superior prediction accuracy compared to any single model while handling various equipment types.
3Device complexity
If failure detection is performed after breakdown, then the system is simpler, but damage prevention is insufficient and product defects occur
Solution Approach 1:
The system performs preliminary failure prediction by analyzing operational data trends before actual failures occur. Multiple deep learning models predict future operational states and identify potential failures in advance, allowing preventive maintenance actions to be taken before damage to workpieces or equipment occurs.
Solution Approach 2:
The system takes preliminary anti-action by predicting failures before they occur and enabling preventive interventions. By identifying degradation trends and predicting future failure states, the system prevents the harmful effects of actual failures including workpiece damage, equipment breakdown, and production losses.
Data Source
AI summary
Provided is a failure prediction device and a failure prediction method for predicting failures in various types of industrial equipment with different operating characteristics or environments using multiple deep learning models selectively, which includes a sensor unit collecting operational data detecting an operating state of the industrial equipment; a data prediction unit provided with multiple deep learning-based prediction models and predicting the operational data of the industrial equipment during a second period after a first period using the operational data collected by the sensor unit; a prediction model selection unit comparing the operational data collected by the sensor unit during the second period with the operational data predicted by the multiple prediction models and selecting any one of the multiple prediction models; and a failure prediction unit predicting the occurrence time of failure of the industrial equipment using the operational data predicted by the selected operational data prediction model.