Sensor Selection Framework for Designing Fault Diagnostics System

Abstract

In a world of rapidly changing technologies, reliance on complex engineered systems has become substantial. Interactions associated with such systems as well as associated manufacturing processes also continue to evolve and grow in complexity. Consider how the complexity of manufacturing processes makes engineered systems vulnerable to cascading and escalating failures; truly a highly complex and evolving system of systems. Maintaining quality and reliability requires considerations during product development, manufacturing processes, and more. Monitoring the health of the complex system while in operation/use is imperative. These considerations have compelled designers to explore fault-mechanism models and to develop corresponding countermeasures. Increasingly, there has been a reliance on embedded sensors to aid in prognosticating failures, to reduce downtime, during manufacture and system operation. However, the accuracy of estimating the remaining useful life of the system is highly dependent on the quality of the data obtained. This can be enhanced by increasing the number of sensors used, according to information theory. However, adding sensors increases total costs with the cost of the sensors and the costs associated with information-gathering procedures. Determining the optimal number of sensors, associated operating and data acquisition costs, and sensor-configuration are nontrivial. It is also imperative to avoid redundant information due to the presence of additional sensors and the efficient display of information to the decision-maker. Therefore, it is necessary to select a subset of sensors that not only reduce the cost but are also informative. While progress has been made in the sensor selection process, it is limited to either the type of the sensor, number of sensors or both. Such approaches do not address specifications of the required sensors which are integral to the sensor selection process. This paper addresses these shortcomings through a new method, OFCCaTS, to avoid the increased cost associated with health monitoring and to improve its accuracy. The proposed method utilizes a scalable multi-objective framework for sensor selection to maximize fault detection rate while minimizing the total cost of sensors. A wind turbine gearbox is considered to demonstrate the efficacy of the proposed framework.

Understanding the Role of Sensor Optimisation in Complex Systems

“…The second meaning, which is the focus of this work, pertains to selecting sensors for integration into a system during the design and build process. In this case, the selection process is geared towards choosing the most suitable sensors for the specific task at hand [ 2 ].…”

“…Figure 1 shows some examples of complex systems. Kulkarni et al discussed an outline for a sensor-selection framework specifically designed for diagnostic applications, and they used wind turbines in their experiment [2]. Defining a step-by-step methodology to rank/identify aircraft sensors for systemlevel condition monitoring and the optimisation of the sensors for onboard/online diagnosis where several domain-specific objective functions and constraints occur is addressed in this study.…”

“…Figure 1 shows some examples of complex systems. Kulkarni et al discussed an outline for a sensor-selection framework specifically designed for diagnostic applications, and they used wind turbines in their experiment [ 2 ].…”

Fusion-Learning of Bayesian Network Models for Fault Diagnostics

“…The correctness of the resulting structure cannot be guaranteed as only the correlation between variables can be estimated [14,15]. Furthermore, because of technical and economic constraints, sensor data may not be able to cover all possible faults [1,16], therefore such BN will be incomplete [6]. Additionally, the causal sufficiency assumption which establishes the theoretical foundation to learn the DAG structure from observational data [15] can be violated if fault causes are incomplete.…”

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

“…The same trend can be seen in monitoring and maintenance systems for electrical machines. This in turn prevents the development of evaluation maps drive conditions for different process equipment topologies [ 8 , 9 ].…”