This paper presents an approach developed to quantify the chaotic behavior for the characterization of electromechanical systems. A technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented. This technique uses a simple peak counting algorithm in the time domain of the current signal to detect faults. To demonstrate the potential of SAC-DM, an experiment is presented where a small BLDC motor at different speeds, with a regular and an unbalanced propeller. The results demonstrated that the SAC-DM was able to detect the speed of the BLDC motor in 99.16% of the cases, and to identify the unbalanced system in 99.79% of the cases, when the speed is at 50%.

The system proposed in this paper aims at monitoring the torque and efficiency in induction motors in real time by employing wireless sensor networks (WSNs). An embedded system is employed for acquiring electrical signals from the motor in a noninvasive manner, and then performing local processing for torque and efficiency estimation. The values calculated by the embedded system are transmitted to a monitoring unit through an IEEE 802.15.4-based WSN. At the base unit, various motors can be monitored in real time. An experimental study was conducted for observing the relationship between the WSN performance and the spectral occupancy at the operating environment. This study demonstrated that the use of intelligent nodes, with local processing capability, is essential for this type of application. The embedded system was deployed on a workbench, and studies were conducted to analyze torque and system efficiency.

Brushless Direct Current (BLDC) motors have been used in a wide range of fields. In some critical applications, failures in these machines can cause operational disasters and cost lives if they are not detected in advance. The classical methods for detecting incipient faults in BLDC motors perform processing of the current signal to obtain the required information. In this work, the SAC-DM (Signal Analysis based on Chaos using Density of Maxima) technique is applied for the first time in the diagnosis of failures of electromechanical systems from sound signals. Wavelet Multiresolution Analysis (WMA) is used to separate a chaotic signal component from the sound emitted by the motor. This work demonstrates that it is feasible to perform dynamic eccentricity diagnosis in BLDC motors by identifying variations of the SAC-DM of the sound signal. The technique exposed in this work requires low computational cost and achieves high success rate. To validate the method, tests were carried out on a small BLDC motor normally used in Unmanned Aerial Vehicle (UAV), demonstrating the ability of the method to detect the speed of the motor in 95.89% of the cases and to detect eccentricity problems at a fixed speed in 88.34% of the cases.

The induction motors are the main equipment used for electromechanical conversion in the world, and are present in almost all production processes, accounting for about two thirds of the industrial electrical consumption. Faults in induction motors can result in operational disasters, and stop entire sectors of a plant, causing economic and human losses. Therefore, the development of appropriate techniques for fault diagnosis in induction motors is critical. Classical methods for induction motors fault diagnosis do not always provide satisfactory results. This paper proposes a hybrid system that uses data obtained from vibration, and current sensors to detect failures at an early stage, since each technique has limitations and disadvantages when used individually. The signals are processed in the frequency and time domain through short time Fourier transform and wavelet multi-resolution analysis, which provides inputs to an intelligent system based on fuzzy logic. The failures due to unbalanced load in the motor shaft and in the motor helix were correctly detected. Experiments with broken bars were also performed, and the system was also validated for this type of failure. The signal processing, and the classification of the failure severity, through fuzzy logic, were developed using Matlab.