Machine Abnormality Prediction Using Dynamic Parameter Learning
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Solution Overview
Problem
Traditional methods for predicting machine abnormalities in factories rely on fixed parameter settings and manual judgments, which are inefficient and lack objective validation, requiring extensive manpower for data marking and becoming less accurate over time due to machine aging.
Innovation Solution
A machine abnormality marking and abnormality prediction system that connects machines to a host with a parameter streaming unit, abnormality reporting unit, and prediction analysis unit, utilizing a neural network classifier to analyze historical and real-time data, generating warnings for maintenance and capacity adjustments to prevent shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed parameter values are used for machine monitoring, then the system is simple to implement, but it cannot effectively reflect the actual states of machines over time due to aging and performance degradation
Solution Approach 1:
The patent implements dynamic parameter ranges instead of fixed values. The system continuously learns and updates the normal operating ranges for machine parameters based on historical data, allowing the monitoring thresholds to adapt automatically as machines age and their performance characteristics change over time.
Solution Approach 2:
The system changes the parameters from static fixed values to dynamic ranges that evolve with machine aging. By using machine learning algorithms, the parameter ranges are continuously adjusted to reflect the actual state of machines at different stages of their lifecycle, maintaining reliability without requiring frequent manual recalibration.
2Measurement precision
If supervised learning with Maximum Likelihood is used to train neural network classifiers, then the mapping relationship between machine data and abnormalities can be learned, but extensive manpower is required for data marking and the system becomes less effective over time due to machine aging
Solution Approach 1:
The system implements self-service through automated unsupervised learning that requires no manual data marking. The neural network automatically learns from raw machine data using algorithms like autoencoders and clustering, eliminating the need for extensive manual annotation while maintaining high detection accuracy. The system continuously adapts to machine aging patterns autonomously.
Solution Approach 2:
The patent replaces the manual mechanical process of data marking with automated computational methods. Instead of human operators manually labeling abnormal data points, the system uses unsupervised learning algorithms to automatically identify anomalies, substituting human labor with intelligent algorithms that scale efficiently.
3Productivity
If one-time data marking is performed, then the initial setup is quick, but the marking data becomes unfit for predicting abnormal states as machines age
Solution Approach 1:
The system ensures continuous learning and adaptation by continuously ingesting new machine data and updating the neural network models in real-time. This continuous action allows the system to maintain prediction accuracy throughout the machine's operational lifecycle, unlike one-time marking approaches that become obsolete as machines age.
Solution Approach 2:
The system performs preliminary adaptation by continuously preparing updated parameter ranges and anomaly detection thresholds based on recent historical data. This ongoing preliminary action ensures the system is always ready to detect abnormalities accurately, adapting proactively to machine aging before critical failures occur.
Data Source
AI summary
The present invention provides a machine abnormality marking and abnormality prediction system connected with a factory host and including a parameter streaming unit connected with the machines, an abnormality reporting unit, a prediction analysis unit, and a neural network classifier. The parameter streaming unit and the abnormal reporting unit collect data of each machine in the factory, and the collected data are compared and analyzed with historical records by the prediction analysis unit. The generated parameter values can be continuously compared with the collected data to predict the state of each machine in the factory, and provide an early warning of possible abnormality or need of maintenance, so that the personnel in the factory can arrange production line maintenance or capacity adjustment in advance or adjust the machine of the factory production line, to avoid occasional shutdown and reduce factory losses.