Equipment Damage Prediction Using Clustered Sensor States
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Solution Overview
Problem
Current predictive maintenance strategies for equipment with moving parts face challenges in determining reliable threshold levels for warnings and alarms, as existing methods are either conservative or insufficient, and lack sufficient failure data and reliable sensor data labels.
Innovation Solution
An apparatus and method utilizing machine learning algorithms trained on calibration sensor data to determine clusters representing different equipment states, allowing for real-time or offline analysis of sensor data to predict equipment damage without prior knowledge, and generating autonomous thresholds based on key condition indicator data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional threshold-based predictive maintenance is used, then equipment monitoring is performed, but reliable threshold levels for warnings and alarms cannot be determined
Solution Approach 1:
The patent replaces traditional mechanical threshold-setting methods with machine learning algorithms that automatically learn optimal thresholds from sensor data. The system uses supervised learning where labeled sensor data (with known equipment states) trains the algorithm to determine warning and alarm thresholds without manual intervention, thereby improving both measurement precision and reliability.
Solution Approach 2:
The patent transforms fixed, pre-defined threshold parameters into dynamic, data-driven thresholds. By changing from static threshold values to adaptive thresholds learned from actual sensor data patterns, the system achieves more accurate and reliable equipment state prediction that adapts to specific equipment characteristics.
2Measurement precision
If more sensor data is collected for better analysis, then equipment state detection improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant features from the sensor data through automated feature selection during the machine learning training process. Instead of processing all raw sensor data, the system identifies and extracts key condition indicators that are most predictive of equipment states, thereby maintaining high detection accuracy while reducing processing complexity.
Solution Approach 2:
The patent performs preliminary data processing and feature extraction during the offline training phase. By pre-processing the sensor data and training the machine learning model beforehand, the system prepares optimized processing pipelines that reduce real-time computation requirements while maintaining high detection accuracy.
3Ease of operation
If manual threshold setting is performed, then system simplicity is maintained, but human error increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically set and optimize its own thresholds through machine learning. The algorithm autonomously learns from labeled sensor data and determines appropriate warning and alarm thresholds without requiring manual configuration, thereby eliminating human error while maintaining operational simplicity through automated decision-making.
4Reliability
If conservative threshold levels are used, then false alarms are reduced, but detection accuracy decreases
Solution Approach 1:
The patent introduces dynamic threshold adjustment based on learned equipment behavior patterns. Instead of using static conservative thresholds, the system adapts thresholds dynamically according to the specific equipment's operational characteristics and degradation patterns, achieving both high reliability and high precision by optimizing thresholds for each equipment instance.
Data Source
AI summary
An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with sensor data for an item of equipment. The processing unit is configured to implement at least one machine learning algorithm, which has been trained on the basis of a plurality of calibration sensor data for the item of equipment. Training of the at least one machine learning algorithm includes processing the plurality of calibration sensor data to determine at least two clusters representative of different equipment states. The processing unit is configured to implement the at least one machine learning algorithm to process the sensor data to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment. The output unit is configured to output the equipment state for the item of equipment.