A Framework for Utilizing Data from Multiple Sensors in Intelligent Mechanical Systems

Electromechanical Actuators (EMAs) are being increasingly used in many applications. There is a need to augment good design of EMAs with continuous awareness of their operational capability and make them ‘intelligent’ for two key objectives: enhancing performance to address exigent task requirements and to track any changes from their ‘as-built and certified’ state for condition-based maintenance. These objectives are achieved using a decision making philosophy where the human system operator supervises EMA operation using performance criteria and decision surfaces; updated by in-situ measurement of the variables of interest via a suite of diverse sensors. However, operational decisions made on the basis of faulty data could result in unwelcome consequences. With unexpected variations in a sensor’s output from its anticipated values, the challenge is to determine if it indicates a problem in the sensor or the monitored system. In addressing this conundrum, it is also essential to account for the inherent uncertainties present in the values being analyzed. To this end, this dissertation presents the development of a novel Sensor and Process Fault Detection and Isolation (SPFDI) algorithm. This provides a framework to utilize data from all the available sensors in a holistic manner to detect any faults in individual sensors or the system components concurrently. The algorithm uses a Bayesian network to model a system; populated with extensive empirical data. The probabilistic foundations of this method allow for incorporating and propagating uncertainties. The construction of a modular testbed and its Bayesian network are discussed in detail. Several design/ operational criteria have been proposed to aid in the creation of more usable networks in the future. The SPFDI algorithm estimates multiple values for each measurand using different combinations of input variables and probabilistic inferencing. These values are compared against those indicated by the corresponding sensors; a difference between them is indicative of a potential problem. Quantitative indicators to track the condition of different system components and sensors, termed as belief values, are modified after each comparison. The final belief values obtained at the end of an iteration of the algorithm provide a definitive indication of the sources of anomalies in the observed data and can provide guidance to the operator on decisions such as whether or not to use data from a particular sensor for updating existing decision surfaces. The representative examples and experimental results confirm the efficacy of the algorithm in detecting and isolating single as well as multiple sensor faults. The algorithm has also been found to be capable of distinguishing between sensor and system/process faults. Special categories of faults and factors that influence the execution characteristics and quality of results from the algorithm were also explored meticulously and suitable modifications have been suggested to enable the algorithm to continue to function effectively in these situations. To demonstrate the flexibility of the proposed SPFDI algorithm, its potential utilization in four broad classes of applications consisting of complex systems monitored by multiple sensors was also explored in this report.