Correlated Sensor Fault Detection for Real-Time Failure Prediction
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Solution Overview
Problem
Existing sensor fault detection methods fail to timely identify sensor failures, leading to inaccurate data, system damage, and costly, time-consuming manual inspections, while conventional approaches often detect faults after they have occurred, lacking the ability to distinguish between sensor faults and operational failures.
Innovation Solution
A data-driven approach using univariate, bivariate, and multivariate anomaly detection, combined with ensemble algorithms, to identify critical sensors, detect faults, predict failures, and implement remediation strategies based on correlated sensor data, incorporating domain knowledge and physics-based models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to detect sensor faults, then detection accuracy may be maintained, but time consumption and operational costs increase significantly
Solution Approach 1:
The system enables sensors to self-diagnose their own health status by analyzing their own output signals against expected ranges and patterns. The sensor module automatically detects anomalies in its own readings without requiring external manual inspection, thereby maintaining detection accuracy while eliminating time-consuming human intervention.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated electronic signal analysis. By using processing modules to continuously monitor and analyze sensor output signals, the system substitutes human operators with automated computational methods, achieving both high detection accuracy and reduced inspection time.
2Reliability
If continuous monitoring of all sensors is implemented, then fault detection timeliness improves, but system complexity and computational resources increase
Solution Approach 1:
The monitoring system is segmented into independent processing modules, each responsible for specific sensor groups or analysis functions. This modular architecture allows continuous monitoring of multiple sensors without creating a monolithic complex system, as each module can operate independently and be managed separately.
Solution Approach 2:
The system implements continuous monitoring selectively based on sensor criticality and anomaly detection needs. Rather than uniformly monitoring all sensors at maximum intensity, the system applies partial monitoring to less critical sensors and excessive (intensive) monitoring to critical sensors, optimizing the balance between detection timeliness and system complexity.
3Productivity
If sensor faults are not detected timely, then system operation continues uninterrupted, but data accuracy deteriorates and system damage may occur
Solution Approach 1:
The system continuously feeds back sensor output signals to processing modules that compare actual readings against expected ranges and patterns. When deviations indicate potential faults, the feedback mechanism triggers alerts or corrective actions, allowing the system to maintain both operational continuity and data accuracy by promptly addressing sensor issues.
Solution Approach 2:
The monitoring system performs preliminary detection of sensor faults before they cause significant data inaccuracies or system damage. By analyzing trends and anomalies in real-time, the system identifies potential failures early and takes preventive actions, ensuring both continuous operation and maintained data precision.
Data Source
AI summary
A method for real-time detection, prediction, and remediation of sensor faults may include receiving sensor data from a plurality of related sensors. The method may also include identifying, for a first sensor in the plurality of related sensors, a set of correlated sensors in the plurality of related sensors. The method may further include detecting a fault in the first sensor based on at least one of the sensor data received from the first sensor, the sensor data received from the set of correlated sensors, and the sensor data received from other sensors. The method may further include implementing a remediation strategy based on the predicted fault of the sensor.