Waveform Anomaly Detection Using Grouped Reference Patterns
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Solution Overview
Problem
Conventional methods for detecting anomalies in industrial plant waveforms often result in improper detection due to variations in environmental conditions and equipment, leading to false alarms and inadequate maintenance scheduling, which can cause equipment damage.
Innovation Solution
A system that classifies waveforms into groups based on electrical and physical properties using a representative waveform pattern, allowing for accurate anomaly detection by comparing test waveforms against reference patterns generated during a learning stage, thereby reducing false alarms and improving maintenance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single model is used to detect anomalies by generalizing normal data patterns, then the detection process is simple, but the accuracy deteriorates due to environmental variations and equipment differences causing false alarms
Solution Approach 1:
The patent segments the detection process into multiple stages: data collection from multiple sources, preprocessing to handle variations, anomaly detection, and validation. This segmentation allows each stage to address specific challenges, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including multiple sensor types as intermediaries to capture comprehensive equipment state, and intermediate processing layers that handle environmental variations before anomaly detection. These intermediaries filter out false alarm sources while preserving genuine anomaly signals.
2Measurement precision
If multiple sensors and processing stages are used to improve anomaly detection accuracy, then the detection precision improves, but the device complexity increases
Solution Approach 1:
The patent implements universal data processing algorithms that can handle multiple sensor types and equipment configurations through a single integrated framework. The anomaly detection model is designed to be multi-functional, accommodating different equipment types and environmental conditions without requiring separate specialized systems for each case.
Solution Approach 2:
The patent merges multiple data sources, processing functions, and validation mechanisms into an integrated anomaly detection system. By combining these elements into a unified architecture, the system achieves high detection accuracy while avoiding the complexity overhead of completely separate systems for each function.
3Reliability
If conventional anomaly detection is used, then false alarms occur frequently, but maintenance scheduling cannot be optimized due to inadequate anomaly identification
Solution Approach 1:
The patent implements feedback mechanisms where anomaly detection results are fed back into the system to refine detection thresholds and adjust maintenance schedules. This continuous feedback loop reduces false alarms over time and enables optimized maintenance scheduling based on actual equipment condition data rather than fixed schedules.
Solution Approach 2:
The patent performs preliminary anomaly detection and risk assessment before critical failures occur, enabling proactive maintenance scheduling. By identifying potential anomalies early and assessing their severity, the system allows maintenance to be scheduled in advance during convenient time windows, avoiding both false alarm disruptions and unexpected equipment failures.
Data Source
AI summary
A method and system for detecting anomalies in waveforms in an industrial plant. During a learning stage, one or more training waveforms are received from sensors monitoring a plurality of equipment in the industrial plant. The one or more training waveforms are used to generate a representative waveform and deviations of the one or more training waveforms from the representative waveform are determined. Based on the deviations, groups are created. A model may be associated with each group for building an expected waveform pattern. When test waveforms are received, based on the electrical and physical properties of the test waveforms, each test waveform is classified into one of the groups. Thereafter, each waveform is compared with the expected waveform pattern associated with the group to which the respective test waveform belongs, to detect the anomaly.