General AI Engine for Predictive Maintenance
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Solution Overview
Problem
Current predictive maintenance approaches face challenges such as high costs, limited generalization due to few failures, lack of high-quality data, and variability in machine-specific sensors, making it difficult for AI models to accurately predict machine failures, especially with unexpected sensor readings.
Innovation Solution
A method that generates an AI predictive maintenance model by receiving machine historical sensor data and failure logs, using a failure labeling model to create training data, and applying an ensemble classifier to predict failures, while also detecting abnormal behavior in real-time, using time series similarities to improve data quality and generalize predictions across different machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a purpose-built AI model is built by hiring data scientists to focus on one case, one machine, and one data stream, then the model can be customized for specific machine conditions, but the cost and time required increase significantly, and the model cannot generalize to other machines
Solution Approach 1:
The patent creates a universal AI model framework that can be applied across multiple machines and failure modes. The model uses a standardized architecture with configurable parameters (failure prediction window, history window, infected interval) that can be adjusted for different machines without requiring retraining or complete model redesign, enabling one model to serve multiple purposes across different equipment types
Solution Approach 2:
The patent employs parameter changes by allowing the same model architecture to adapt to different machines through configurable parameters such as failure prediction window, history window size, and infected interval. These parameters can be adjusted based on specific machine characteristics without changing the fundamental model structure, enabling generalization while maintaining customization
2Reliability
If AI models are trained on historical failure data, then failure patterns can be learned and predicted, but the limited number of failures makes it hard for the model to generalize and perform reliably
Solution Approach 1:
The patent applies preliminary action by pre-defining the failure prediction window and infected interval parameters before training. The model is configured to look ahead by a predetermined time window from potential failure points and to exclude the infected interval where actual failure occurred, allowing the model to learn from limited failure data by focusing on the critical pre-failure period and avoiding biased training from post-failure data
Solution Approach 2:
The patent uses partial action by training the model on a selective subset of historical data focused on the failure prediction window period rather than using all available historical data. This partial approach concentrates the limited failure data points into the most relevant time window, improving learning efficiency and generalization despite the small quantity of failure examples
3Measurement precision
If machines have their own specific sensors for detecting failures, then each machine can be monitored with appropriate detection capabilities, but the variability in sensor types and configurations makes it difficult to create a unified predictive maintenance model
Solution Approach 1:
The patent introduces an intermediary layer in the form of a standardized data processing pipeline that sits between the machine sensors and the AI model. This intermediary handles the variability in sensor configurations by normalizing and transforming diverse sensor data into a unified representation that the model can process consistently, bridging the gap between machine-specific sensor variations and model requirements
Solution Approach 2:
The patent applies parameter changes by using a configurable feature extraction and transformation pipeline that can adapt to different sensor types. The model architecture includes adjustable parameters that allow it to handle different numbers of sensors, different sensor frequencies, and different measurement units by transforming them into a standardized internal representation, maintaining measurement precision while achieving sensor agnosticism
4Productivity
If corrective maintenance replaces parts as they fail, then system parts are used until failure with minimal intervention, but it results in downtime, manpower costs, and unscheduled repairs
Solution Approach 1:
The patent applies preliminary action by predicting failures before they actually occur, allowing maintenance activities to be scheduled in advance. The model identifies potential failure conditions by analyzing patterns in sensor data leading up to actual failures, enabling organizations to perform maintenance during planned downtime rather than reacting to unexpected failures, thus reducing production loss
5Reliability
If preventive maintenance replaces parts before they fail, then unexpected breakdowns are avoided, but parts are not used as much resulting in additional costs
Solution Approach 1:
The patent applies preliminary action by predicting the exact timing of failures based on actual machine degradation patterns, allowing parts to be replaced only when necessary rather than on a fixed schedule. This enables maintenance to be performed at the optimal moment before failure occurs, maximizing part utilization while preventing breakdowns
Solution Approach 2:
The patent uses feedback by continuously monitoring machine condition through sensor data and comparing actual performance against predicted failure patterns. The model provides feedback on the likelihood of imminent failure, allowing maintenance decisions to be based on real-time condition information rather than predetermined schedules, optimizing both reliability and part utilization
Data Source
AI summary
Systems and methods for building predictive maintenance artificial intelligence models are disclosed that can predict machine failures in advance using the historical sensor readings and machine failure logs. The engine utilizes a variety of data science pipelines to process and model historical sensor data for both learning failure patterns to predict them and learning sensors' normal behavior to detect the abnormality when it happens. The engine is able to automatically differentiate between normal sensor signals and failure(indicators) signals, as well as artificially generate failure signals to generalize the prediction for rarely occurring failures.