Anomaly Detection Server for Industrial Machinery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing anomaly detection systems in production environments rely heavily on human expertise, leading to scalability issues, risk of expertise loss, biases in detection, and limited applicability across varying machinery types, resulting in inefficiencies and increased downtime.
Innovation Solution
Anomaly detection server utilizing machine learning models trained with analysis vectors from sensors to identify discrepancies in machine states, sequences, and timing, enabling automated detection of anomalies across diverse machinery types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expertise is used for anomaly detection, then detection accuracy for specific machinery is improved, but scalability and adaptability to varying equipment types deteriorate
Solution Approach 1:
The patent creates a digital twin or virtual model of the machinery that replicates normal operational patterns. This digital copy is trained using historical data from multiple equipment types, enabling the system to generalize anomaly detection across different machinery without requiring human expertise for each specific type. The digital twin serves as a reusable template that adapts to various equipment through data-driven learning.
Solution Approach 2:
The system transforms qualitative human expertise into quantitative parameters and features that can be processed by machine learning models. By converting operational knowledge into measurable parameters (vibrations, temperatures, operational sequences), the system achieves both high detection accuracy and broad adaptability across different machinery types through parameter-based analysis rather than equipment-specific human judgment.
2Measurement precision
If human-based anomaly detection systems are used, then expertise-based accuracy is improved, but reliability due to expertise loss and unavailability deteriorates
Solution Approach 1:
The system captures and stores operational patterns and anomaly indicators directly from the machinery during normal operation, building an autonomous detection capability that does not depend on human experts. The machinery essentially teaches the system about itself through continuous data collection and machine learning, creating a self-sufficient anomaly detection system that maintains reliability independent of human expertise availability.
Solution Approach 2:
The system performs preliminary training during normal operational periods by collecting and analyzing data to establish baseline patterns before anomalies occur. This advance preparation creates a ready-to-use detection model that can immediately identify anomalies when they occur, eliminating the need for human experts to be present or available at the time of detection while maintaining high accuracy.
3Productivity
If automated anomaly detection systems are implemented, then scalability is improved, but effectiveness across varying equipment types deteriorates
Solution Approach 1:
The patent develops a universal anomaly detection framework that can handle multiple types of machinery through a common machine learning architecture. The system uses equipment-agnostic features and patterns that can be applied across different equipment types, allowing scalable deployment while maintaining effectiveness. The universal model is trained on diverse data from various machinery types, enabling it to generalize effectively across the entire equipment fleet.
Data Source
AI summary
Examples of the disclosure enable detecting an anomalous state in a machine. The method is performed by an anomaly detection server. The method includes receiving training analysis vectors associated with a monitored machine during a training period. The method also includes applying the training analysis vectors to a machine learning model to create a trained machine learning model configured to describe normal states in the monitored machine as indicated by training analysis vectors. The method further includes receiving monitoring analysis vectors associated with the monitored machine during a monitoring period. The method also includes applying the monitoring analysis vectors to the trained machine learning model to identify at least one discrepancy indicating an anomalous state in the monitored machine. The method further includes transmitting an alert indicating that an anomaly is detected in the monitored machine.