  Several methods have been proposed to identify which sensor sets are optimal
for finding and localizing faults under different conditions for mechanical
equipment. In order to preserve acceptable performance while minimizing costs,
it is crucial to identify the most effective set of sensors available.
Nevertheless, some sensor sets can behave differently in fault detection
because of uncertainty in the measurement data contaminated by noise
interference. This paper develops new sensor selection models using Data
Envelopment Analysis (DEA), which has proven helpful as a management approach
for determining an optimal number of sensors, associated costs, and sensor
performance in the fault diagnosis. We propose four linear optimization models
for sensor selection to design the fault detection system. The validity of the
presented models is demonstrated using a vibration dataset collected from a
gearbox. Based on the case study results, the proposed methods are effectively
superior to the other comparison sensor selection methods in fault detection
accuracy.
