Shunfeng Cheng

Sensor Systems for Prognostics and Health Management

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.

A Wireless Sensor System for Prognostics and Health Management

This paper introduces a novel radio-frequency-based wireless sensor system and describes its prognostics and health management functions. The wireless sensor system includes a radio frequency identification sensor tag, a wireless reader, and diagnostic-prognostic software. The software uses the sequential probability ratio test with a cross-validation procedure to detect anomalies, assess degradation, and predict failures. The prognostic performance of the sensor system is demonstrated by a field application.

A fusion prognostics method for remaining useful life prediction of electronic products

Prognostics and health management methods can provide advance warning of failure; reduce the life cycle cost of a product by decreasing inspection costs, downtime, and inventory; and assist in the design and logistical support of fielded and future electronic products. Traditional prognostic methods, such as data-driven methods and physics of failure methods have some limitations. This paper presents a fusion prognostics method, which fuses data-driven methods and physics of failure methods to predict the remaining useful life of electronic products. This method integrates the advantage and overcome the limitations of the data-driven methods and the physics of failure methods to provide better predictions.

Using cross-validation for model parameter selection of sequential probability ratio test

The sequential probability ratio test is widely used in in-situ monitoring, anomaly detection, and decision making for electronics, structures, and process controls. However, because model parameters for this method, such as the system disturbance magnitudes, and false and missed alarm probabilities, are selected by users primarily based on experience, the actual false and missed alarm probabilities are typically higher than the requirements of the users. This paper presents a systematic method to select model parameters for the sequential probability ratio test by using a cross-validation technique. The presented method can improve the accuracy of the sequential probability ratio test by reducing the false and missed alarm probabilities caused by improper model parameters. A case study of anomaly detection of resettable fuses is used to demonstrate the application of a cross validation method to select model parameters for the sequential probability ratio test.

Failure Precursors for Polymer Resettable Fuses

Resettable fuses have been widely used in overcurrent or overtemperature circuit protection designs in computers, automotive circuits, telecommunications equipment, and medical devices. Abnormal behavior of a resettable fuse can damage a circuit. This paper identifies and experimentally assesses the failure precursor parameters of a polymer positive temperature coefficient resettable fuse. It is shown that the degradation of the resettable fuse can be monitored, detected, and predicted based on the monitoring of these precursor parameters.

Sensor System Selection for Prognostics and Health Monitoring

Data collection is an essential part of prognostics and health monitoring, and often requires the use of sensor systems to measure environmental and operational parameters. In this paper, the considerations for sensor system selection for prognostics and health monitoring implementation are discussed and some state-of-the-art sensor systems for prognostics are described. Finally, emerging trends in sensor system technologies are presented.

Anomaly Detection of Polymer Resettable Circuit Protection Devices

As circuit protection devices, failure or abnormal behavior of polymer positive-temperature-coefficient resettable devices can cause damage to circuits. It is necessary to detect anomalies in the resettable circuit protection devices to provide early warning of failure and avoid damage to a circuit. In this paper, a novel anomaly detection method, the cross-validation-based sequential probability ratio test, is developed and applied to the failure precursor parameters of the resettable circuit protection devices to conduct anomaly detection. The cross-validation-based sequential probability ratio test integrates the advantages of both the sequential probability ratio test for in situ anomaly detection and the cross-validation technique for model parameter selection to reduce the probability of false and missed alarms in anomaly detection. The cross-validation-based sequential probability ratio test solves the model parameter selection difficulty of the traditional sequential probability ratio test and improves its performance in anomaly detection.

A Radio Frequency Sensor System for Prognostics and Health Management

This paper introduces a radio frequency identification sensor system and its application for prognostics and health management. In prognostics and health management applications, the radio frequency identification sensor system collects data and transfers the data wirelessly into computers. The data then is analyzed by failure detection and prediction algorithms. The performance of the sensor system for prognostics and health management is demonstrated by a field application.© 2009 ASME