  Overloading in DC servo motors is a major concern in industries, as many
companies face the problem of finding expert operators, and also human
monitoring may not be an effective solution. Therefore, this paper proposed an
embedded Artificial intelligence (AI) approach using a Convolutional Neural
Network (CNN) using a new transformation to extract faults from real-time input
signals without human interference. Our main purpose is to extract as many as
possible features from the input signal to achieve a relaxed dataset that
results in an effective but compact network to provide real-time fault
detection even in a low-memory microcontroller. Besides, fault detection method
a synchronous dual-motor system is also proposed to take action in faulty
events. To fulfill this intention, a one-dimensional input signal from the
output current of each DC servo motor is monitored and transformed into a 3d
stack of data and then the CNN is implemented into the processor to detect any
fault corresponding to overloading, finally experimental setup results in
99.9997% accuracy during testing for a model with nearly 8000 parameters. In
addition, the proposed dual-motor system could achieve overload reduction and
provide a fault-tolerant system and it is shown that this system also takes
advantage of less energy consumption.
