FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Compact information technology enabled systems for intelligent process monitoring

The use of computers in industrial process applications is ever-increasing. Initially used to provide help to the machine operator, their application has evolved through automatic process control to monitoring of process health and performance. The latter, together with the quality control of the end product directly affect plant economics and ultimately the financial viability of the company. The research reported in this thesis is a contribution towards providing a cost-effective method of calculating a measure of the current health of a process and predicting any maintenance issues that may arise in the near future. Embedded systems are utilised and the monitoring system is designed to work automatically with a minimal input from the operator. This eliminates the need for peripherals such as keyboards, mice, and monitors thus reducing the overall system price and footprint. User interfaces are provided via the Internet and mobile phones giving remote access to multiple users. Single chip microcontrollers are at the heart of the embedded system rather than microprocessors, thereby reducing the relative system cost and size at the expense of localised processing power. The microcontrollers are distributed in a hierarchical network to attain the required processing power whilst minimising data storage and communications and to improve signal-to-noise ratios. The Controller Area Network (CAN) bus was selected, and used for the inter-microcontroller communications, for its robust performance in noisy environments. In the developed system architecture, each microcontroller node acquires one of the required process sensor signals and applies initial signal processing. A novel sweeping filter technique is developed to perform frequency analysis using the microcontrollers. The processed data from all nodes are then combined using situation-based criteria to reach conclusions often not evident from single sensor data. The Internet-based system is provided with the capability to upload any monitoring software or updates. Plug & play capability of the monitoring nodes is also provided so that the system can be seamlessly adapted to new or changed applications. The design and development of the system are detailed along with its deployment on various applications. Fault detection, isolation, and prediction were achieved on batch and continuous processes. A machine tool application proved the frequency analysis and network traffic reduction capabilities. On-line monitoring of an industrial valve was also performed