Industrial Machine Failure Prediction Using Operation Mode Indicators
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Solution Overview
Problem
Current predictive maintenance models for industrial machines face challenges in accuracy due to limited data, expert annotation issues, and differences in expert assessments, leading to potential incorrect predictions and unnecessary downtime.
Innovation Solution
A module arrangement with sub-ordinated modules that process machine data to determine intermediate status indicators and operation mode indicators, which are then used by an output module to predict failures, enhancing prediction accuracy by considering different operation modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single functional module is used for failure prediction, then the device complexity is low, but the prediction accuracy is insufficient
Solution Approach 1:
The prediction system is divided into multiple specialized modules: operation mode classifier module for identifying operation modes, first and second sub-ordinated modules for processing different aspects of machine data, and an output module for generating predictions. Each module focuses on specific tasks, improving overall prediction accuracy while managing complexity through functional segmentation
2Measurement precision
If multiple sub-ordinated modules are arranged in hierarchy, then the prediction accuracy is improved, but the training complexity increases
Solution Approach 1:
The operation mode classifier module is trained first to establish operation mode classifications before the other modules are trained. This preliminary action creates a foundation that simplifies subsequent training processes, as the first and second sub-ordinated modules can rely on the pre-established mode classifications rather than learning them simultaneously
3Reliability
If operation mode classification is integrated, then the prediction reliability is improved, but the data processing complexity increases
Solution Approach 1:
The operation mode classifier module acts as an intermediary that processes raw machine data to generate operation mode indicators, which then serve as input features for the first and second sub-ordinated modules. This intermediary layer simplifies the data processing architecture by creating standardized intermediate representations that improve prediction reliability
Data Source
AI summary
A computer-implemented failure predictor has a module arrangement (373) with first and second sub-ordinated modules (313, 323) that are sub-ordinated to an output module (363). The first and a second sub-oriented modules process data from an industrial machine to determine first and second intermediate status indicators. A third sub-oriented module (333) determines an operation mode indicator, and the output module (363) processes the status indicators and the operation mode indicator to predict a failure of the industrial machine. The module arrangement has been trained by cascaded training to comprises to train the sub-ordinated modules (312, 322, 332), to subsequently operate the trained sub-ordinated modules, and to subsequently train the output module.