## Non-Invasive Method for In-Service Induction Motor Efficiency Estimation Based on Sound Acquisition
###### Abstract  
Induction motors (IMs) are present in practically all production processes and account for two-thirds of the energy consumption in industrial settings. Therefore, monitoring them is essential to prevent accidents, optimize production, and increase energy efficiency. Monitoring methods found in the literature require a certain level of invasiveness, causing some applications to be unfeasible. In the present study, a new completely non-invasive method implemented in an embedded system performs the embedded processing of the sound signal emitted by an in-service IM to estimate speed, torque, and efficiency. Motor speed is estimated from the analysis in the frequency domain using the Fourier Transform. Torque and efficiency are estimated from the speed and motor nameplate information. To perform the tests and validate the proposed method/system, a workbench with a controllable torque was used. The workbench was also equipped to allow the results to be compared with the airgap torque method. The results indicate a high accuracy for the nominal load (error of approximately 1%) in the measurement of the efficiency and torque, and a mean relative error of 0.2% for the speed. Keywords:induction motor; efficiency estimation; sound signal processing +
Footnote †: journal: Applied Soft  
is often precarious. Being that one of the reasons the literature indicates that researchers are seeking to develop techniques associated with low invasiveness for the in-service motor efficiency estimation. Methods for the in-service efficiency estimation of an IM can be differentiated according to the type of instrumentation used, the invasiveness, and the estimation accuracy levels. Usually, the most invasive methods are the most accurate [6]. The simplest and also the least accurate method found in the literature for these motors is the slip method, which only requires a rotation sensor on the shaft. Recently, techniques that apply a shaft speed sensor, a temperature sensor, and an induction motor database in association with optimization algorithms have been used with a high accuracy level to determine the efficiency of IMs with distorted unbalanced voltages using an approach based on IEEE Std 112 F1 [7; 8]. [PERSON] et al. [9] proposed a method using an accelerometer that estimates the online efficiency using the nameplate and manufacturer's information. The methods based on electric measurements found in the literature include the current method [10], IEEE Std 112 E1 method [6], and the air gap torque method. The air gap torque method (AGT), due to its low invasiveness and relatively low uncertainty, has been extensively studied and improved over the years. [PERSON] et al. [10] proposed an IM efficiency estimation based on the AGT, which was improved by [PERSON] et al. [11], allowing the sensorless measurement of the shaft rotational speed and online stator resistance estimation. [PERSON] et al. [12] adapted the AGT for the efficiency estimation in dynamic systems. In turn, [PERSON] et al. [13] presented a new technique to obtain the stator resistance, aiming at improving the efficiency estimation using the AGT. Still using measurements of motor voltages and currents, different independent algorithms have been implemented for the determination of the in situ efficiency. [PERSON] et al. [14] and [PERSON] et al. [15] utilized a bacterial foraging optimization algorithm, and [PERSON] and [PERSON] et al. [16] utilized an evolutionary search algorithm. The use of the sound signal for industrial applications found in the literature is restricted to studies directed to the noise mitigation [17] and the diagnostic of faults in combustion engines [18; 19], electric motors [20; 21], and gearboxes [22; 23]. Particularly for applications involving three-phase induction motors, [PERSON] et al. [24] developed an algorithm applied to the sound and vibration signals of a motor based on the Complete Ensemble Empirical Mode Decomposition and marginal frequency of the Gabor to detect faults in IM.