Sensor Tag Autoencoder for Early Manufacturing Risk Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturers face challenges in managing operating assets due to reliance on scheduled or reactive maintenance, leading to lost revenue, excess costs, shortened asset life, poor product quality, and safety risks.
Innovation Solution
A system that predicts and detects anomalies in a processing pipeline by using machine learning methods to identify indicative tags from sensor data, processing these data with an autoencoder to detect deviations, and transmitting alerts for proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scheduled or reactive maintenance is used to manage operating assets, then maintenance can be performed, but lost revenue from deferred production, excess costs, shortened asset life, poor product quality, and personnel safety risks occur
Solution Approach 1:
The system performs preliminary actions by continuously monitoring asset data and predicting failures before they occur. The anomaly detection system analyzes sensor data, operational parameters, and maintenance records to identify potential issues in advance, enabling proactive maintenance scheduling that prevents production interruptions and extends asset life.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting data from sensors and operational systems, analyzing this data through machine learning models, and using the results to adjust maintenance schedules and operational parameters. This closed-loop feedback enables continuous improvement of asset reliability while optimizing production output.
2Ease of repair
If reactive maintenance is performed after failures occur, then asset repairs can be made, but excess costs and shortened asset life result
Solution Approach 1:
The system performs preliminary diagnostic actions by continuously monitoring asset conditions and predicting failures before they occur. The anomaly detection system identifies early signs of deterioration, allowing maintenance to be scheduled at optimal times that extend asset life while ensuring repairs are made when most effective.
Solution Approach 2:
The system enables self-service through automated monitoring and prediction capabilities that continuously assess asset health without requiring constant human intervention. The machine learning models automatically detect anomalies and generate maintenance recommendations, allowing the asset management system to serve itself proactively.
3Measurement precision
If more sensors and data collection are implemented to improve anomaly detection accuracy, then prediction accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system achieves multi-functionality by using a unified machine learning framework that processes diverse data types from multiple sensors simultaneously. The anomaly detection model handles various data formats and sensor inputs through a single integrated system, reducing overall complexity while maintaining high detection accuracy across multiple asset types and failure modes.
Solution Approach 2:
The system dynamically adjusts parameters such as detection thresholds, data sampling rates, and model complexity based on asset criticality and operational conditions. This allows the system to optimize the balance between detection accuracy and computational resources, scaling the level of monitoring detail to match the importance and risk profile of each asset.
Data Source
AI summary
The present disclosure provides system, methods, and computer program products for predicting and detecting anomalies in a subsystem of a system. An example method may comprise (a) determining a first plurality of tags that are indicative of an operational performance of the subsystem. The tags can be obtained from (i) a plurality of sensors in the subsystem and (ii) a plurality of sensors in the system that are not in the subsystem. The method may further comprise (b) processing measured values of the first plurality of tags using an autoencoder trained on historical values of the first plurality of tags to generate estimated values of the first plurality of tags; (c) determining whether a difference between the measured values and estimated values meets a threshold; and (d) transmitting an alert that indicates that the subsystem is predicted to experience an anomaly if the difference meets the threshold.