An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances

In general, unexpected failures in sensorless brushless DC (BLDC) motors can
result in production downtime, costly repairs, and safety concerns. BLDC
motors are commonly used in home appliances, the medical sector, aerospace,
small-scale, and large-scale industries under uncertain operating conditions.
Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can
play a very important role in increasing their performance, reliability, robustness
control, and operational safety under uncertain operating conditions in
critical real-time applications. To satisfy these issues of hall effect sensor, misplacement
of a hall-effect sensor, inverter IGBT open-switch fault diagnosis,
failure of hall effect sensor, lack of robustness speed control of BLDC motor,
which has received substantial interest in academic and industry sectors to
establish the proposed work optimization techniques approach FDD strategy
for speed control of sensorless BLDC motor under uncertain operating conditions.
The proposed optimization techniques such as Bat Algorithm (BA), Grey
Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA)
approach FDD strategies for BLDC motor drives. These FDD strategies simulated
by the above optimization techniques on a sensorless BLDC motor with
numerical Matlab/Simulink 2020a simulation results are verified. From the
simulation results, out of three optimization techniques, the WOA-based FDD
strategies are very effective for both bearing and stator winding faults detection
and diagnosis in sensorless BLDC motor drives.