Photosensor Fault Detection and Localization in Lighting Control Systems
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Solution Overview
Problem
Advanced lighting control systems face challenges in monitoring and identifying faulty light sensors, leading to compromised performance and increased power consumption due to undetected abnormalities such as user tampering, dust, electronic degradation, and communication issues, with existing solutions lacking effective detection and corrective actions.
Innovation Solution
A system comprising a network of sensors connected to a computing device with a training, detecting, and locating subsystem, which determines the optimum operation of the lighting control system, identifies faulty sensors, and locates them based on fault indicators, using historical data to estimate joint probability distributions and detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advanced lighting control systems are implemented to improve energy savings and building performance, then energy efficiency and productivity are improved, but system complexity increases and makes fault detection difficult
Solution Approach 1:
The patent implements a feedback mechanism where sensor readings are continuously monitored and compared against expected values derived from historical data and environmental models. When deviations are detected, the system generates fault indicators that trigger alerts or automatic corrective actions, enabling continuous self-verification without increasing operational complexity
Solution Approach 2:
The system performs self-diagnosis by automatically detecting sensor faults through statistical analysis of sensor readings against predicted values. The fault detection subsystem operates autonomously, identifying problematic sensors and notifying the control system without requiring manual intervention, thus maintaining energy efficiency while managing system complexity
2Reliability
If sensor performance is monitored to maintain system reliability, then reliability is improved, but measurement and detection difficulty increases due to multiple fault sources
Solution Approach 1:
The patent introduces an intermediary expected value calculation that serves as a reference benchmark. This expected value is derived from historical sensor data and environmental models, acting as a mediator between raw sensor readings and fault detection logic. By comparing actual readings against this intermediary reference, the system can reliably detect faults without directly analyzing complex multi-source fault patterns
Solution Approach 2:
The system changes the parameter being monitored from raw sensor values to fault indicators derived from statistical deviations. By transforming the measurement parameter from absolute sensor readings to relative deviations from expected values, the system simplifies fault detection while maintaining high reliability in identifying sensor abnormalities
3Productivity
If faulty sensors are not detected, then system operation continues uninterrupted, but performance is compromised and power consumption increases
Solution Approach 1:
The system performs preliminary fault detection by continuously comparing sensor readings against expected values before faults significantly impact system performance. By detecting issues early through statistical analysis, the system can take corrective action preemptively, maintaining optimal operation and preventing energy waste while ensuring continuous productivity
Data Source
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AI summary
A method and system for monitoring sensors of a lighting control system. The method comprises performing a training of a plurality of sensors of the lighting control system (210) to determine a joint probability distribution function (PDF) of the illuminance at a given time t; collecting parameters from the training and storing the parameters in a prior data storage (S212); observing illuminance of the plurality of sensors (S222); determining if there is at least a faulty sensor from among the plurality of sensors based in part on the parameters stored prior data storage (S224); and locating a faulty sensor based on the determination of the existence of the at least a faulty sensor and the prior data (S232, S234).