Machine Failure Prediction Using Unsupervised Sensor Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine monitoring systems fail to accurately predict failures due to reliance on predetermined rules, neglecting collected data, requiring specialized operators, and leading to unnecessary maintenance and downtime, while current solutions often miss or inaccurately determine failures.
Innovation Solution
An unsupervised machine learning approach that analyzes sensory inputs from machines to identify patterns and predict future failures, using preprocessing and unsupervised machine learning models to select optimal methods for detecting anomalies and generating adaptive thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predetermined rules are used for monitoring machine failures, then the monitoring system can identify failures quickly, but it only checks particular key parameters while ignoring the rest of the collected data
Solution Approach 1:
The patent extracts and analyzes specific features from the collected sensory data that are most relevant to failure prediction, rather than using all raw data or ignoring data entirely. The system identifies and focuses on critical parameters that indicate potential failures.
Solution Approach 2:
The system transforms raw sensory data into meaningful features by changing parameters through signal processing and feature extraction techniques. This allows the system to convert large volumes of raw data into compact, informative representations that capture failure indicators.
2Loss of time
If periodic testing at predetermined intervals is used, then maintenance can be scheduled in advance, but it results in wasted materials and expenses replacing parts that are still functioning properly
Solution Approach 1:
The system performs preliminary analysis of sensory data to predict future failures before they actually occur. By detecting early signs of degradation and predicting when failure will happen, the system enables maintenance to be scheduled just in time, avoiding both premature replacement and unexpected failures.
Solution Approach 2:
The system continuously monitors sensory data and provides feedback about the actual condition and degradation trends of machine components. This feedback loop allows dynamic adjustment of maintenance timing based on real-time condition assessment rather than fixed schedules.
3Reliability
If existing monitoring systems are used to identify failures, then failures can be detected after or immediately before downtime begins, but they cannot predict failures in advance to prevent downtime
Solution Approach 1:
The system performs preliminary detection of failure indicators by analyzing sensory data for early signs of degradation. It predicts future failures before they occur, enabling preventive maintenance that avoids downtime entirely rather than just detecting failures after they happen.
Solution Approach 2:
The patent replaces traditional mechanical monitoring approaches with data-driven predictive analytics. By using machine learning algorithms to analyze sensory data patterns, the system substitutes conventional reactive monitoring with proactive failure prediction capabilities.
4Measurement precision
If dedicated testing equipment and specialized operators are required, then accurate failure detection can be achieved, but the system becomes inconvenient and costly with potential human error
Solution Approach 1:
The system uses standard sensory equipment already present on modern machines (temperature sensors, vibration sensors, current sensors) rather than requiring dedicated specialized testing equipment. The same sensors serve multiple functions including operation monitoring and failure prediction.
Solution Approach 2:
The system performs automated analysis of sensory data using machine learning algorithms, eliminating the need for specialized operators to interpret results. The system self-diagnoses potential failures by automatically detecting patterns in the sensory data.
Data Source
AI summary
A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.