Passenger safety is of utmost importance in the automotive industry. Hence, the
health of the components, especially the brake system, should be effectively
monitored. On account of the significance of artificial intelligence in recent
times, any brake fault resulting during operation can be accurately detected
using a combination of advanced measurement techniques and machine learning
algorithms. The current study focuses on developing and evaluating a robust
framework to quantify and classify the faults of a general automotive drum
brake. For this purpose, a new experiment for a drum brake, which can be
operated under a controlled environment with known levels of faults, is
developed. The experiment is instrumented to measure the fundamental dynamic
signals (such as brake torque, the angular velocity of the brake drum, and brake
shoe accelerations) during a braking event. The response signals from several
experiments with various faults and operating conditions serve as the input
dataset for establishing the fault quantification algorithm. Multiple variants
of this algorithm are devised using different subsets of the input dataset. The
selection of features in each variant is done through sensitivity-based
segregation with the help of artificial neural networks. The performance of all
the variants is comparatively evaluated, and the best among them is determined
based on the fault quantification error. Finally, fault classification is
carried out using the best variant after establishing the classification
thresholds based on the confusion matrix. The following are the novel aspects of
this work: (i) design and development of a laboratory experiment for drum brakes
that can imitate a real-life braking condition; (ii) measurement of the dynamic
response of the system during a typical braking event with a controlled type and
level of brake fault using appropriate instrumentation; (iii) estimation of the
magnitude of multiple brake faults, in addition to their classification; and
(iv) identification of the critical vibration measurements necessary for
detecting faults in brakes. In addition, the physical insights into the brake
system response, selected features, and the fault quantification algorithm are
presented. The proposed framework can also be implemented for fault diagnosis in
different automotive subsystems by using an equivalent experiment. The goal of
the current work is to develop a simple in situ tool for monitoring the health
and diagnosing faults in automotive drum brakes. When integrated with other
smart diagnostic and prognostic features, this tool can help automotive
manufacturers improve passenger safety.
