# Recognition of Acoustic Signals of Commutator Motors
## 1 Introduction  
Commutator motors (CMs) are the most frequently used electrical motors for devices used at home, for example, electric impact drills, angle grinders, cordless drills, hair dryers, blenders, etc. CMs are also used in the energy industry, material industry, construction industry, mining industry, transport industry, and by construction and renovation companies. Sometimes, faults of devices can happen. An undetected fault can lead to the damage of the whole device. Frequent faults of commutator motors are: bearing faults, damaged gears, damaged teeth, short-circuits, and damaged electric motor brushes. Bearings hold the shaft of the CM, but they wear out after years of use. Most of these faults can stop the motor. Then, there is a loss of time associated with fixing the damaged motor. It is essential for industrial motors. A production line (consisting of motors) can be stopped for a longer time for repair.  
The above constitutes an essential reason for developing fault-detection methods. Diagnostic methods can use various signals, for example: ultrasound, acoustic signals, current signals, magnetic signals, vibration, video images, and thermal images. There is also the possibility of using oil analysis. Ultrasound is used for detecting faults of motors, pumps, and bearings [1; 2; 3; 4]. However, the cost of an ultrasound generator is about $1000. The analysis of acoustic emissions (in the range 30-16,000 Hz) is noninvasive and low-cost [5; 6; 7; 8; 9; 10; 11; 12]; however, its main disadvantages are the appearanceof background noises and reflected sounds. The analysis of thermal images is also a common diagnostic technique [13; 14; 15], which is used for electrical faults such as shorted coils and broken bars of the motor. The analysis of electric currents is also used for the fault diagnosis of motors [16; 17; 18; 19; 20; 21]. Similarly to the analysis of thermal images, it can be used for electrical faults. Vibration analysis is often used for the condition monitoring of motors [22; 23; 24; 25; 26; 27; 28]. Vibration analysis can detect many types of faults, such as rotor and stator faults, mechanical faults, gear faults, cracks, bearing faults, damaged teeth of sprockets, etc. Moreover, a vibration signal is measured immediately.  
This paper deals with the fault detection of an electric impact drill and blender using a smartphone. Considering that the electric impact drill and blender consisted of commutator motors and other mechanical parts such as gears, sprockets, etc., five states of the electric impact drill were analysed: healthy (Figure 1), with a damaged gear train (Figure 2), with a faulty fan with five broken rotor blades (Figure 3), with a faulty fan with 10 broken rotor blades (Figure 4), and with a shifted brush (motor off) (Figure 5); and three states of the blender were analysed: healthy (Figure 6), with a faulty fan with 2 broken rotor blades (Figure 7), and with a faulty fan with 5 broken rotor blades (Figure 8).  
Figure 4: Electric impact drill with a faulty fan (10 broken rotor blades; indicated by the blue box).  
Figure 5: Electric impact drill with a shifted brush (motor off; indicated by the blue box).  
Figure 3: Electric impact drill with a faulty fan (5 broken rotor blades; indicated by the blue box).  
The developed approach used a smartphone as the measuring device. Acoustic signals were recorded by the smartphone. Next, the signals were processed by a method of amplitude normalization, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio 27% Multiexpanded 4 Groups--described more in detail in Section 2.1) and nearest mean and SVM classifiers. The results of the fault detection for the electric impact drill and blender are presented in Section 3.  
Figure 8: Blender with a faulty fan (5 broken rotor blades; indicated by the blue box).  
Figure 6: Healthy blender.  
Figure 7: Blender with a faulty fan (2 broken rotor blades; indicated by the blue box).