Condition monitoring of electrical machines using low computing power devices

The diagnosis of electrical motors through the detection of fault frequency signatures in the current's spectrum has become an established standard in the field of industrial maintenance systems. Nevertheles, its implementation on devices with low computing power remains a practical challenge. Industrial controllers, such as programmable logic controllers, or modern, low cost controller hardware, such as the Arduino or the Raspberry Pi open source hardware proposals, lack both the on-board memory and the high speed data acquisition hardware to perform an accurate spectral analysis of the machine's current, in order to identify the spectral components produced by each type of fault. In this paper, a signal conditioning unit, based on a novel downsampling method of the current, is presented. This unit reduces the amount of current samples that must be processed by the diagnostic unit to a mere sample per current cycle, maintains the sub-hertz accuracy needed to resolve fault, and converts the mains component into a constant value that can be easily eliminated without using any additional filter. Besides, it is implemented using low cost devices, just resistors and operational amplifiers. The proposed method is theoretically developed in this paper, and it has been validated using induction motors with broken bars fed directly by the mains or through variable speed drives.