# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 1 Introduction
### Related Work  
Train door control systems use a DC motor to operate the doors. Obstruction of the train door affects the motor current signal. Motor current is an excellent parameter for detecting the mechanical load on the motor. If an object obstructs the door's movement, the mechanical load on the motor abruptly increases, and this in turn affects the motor current signal. As shown in Table 1 below, various methods have been proposed in the literature in which motor electrical parameters are employed to detect obstacles. [PERSON] [5] demonstrated that a decrease in the back electromotive force (EMF) indicates that a motor has stalled. This method is effective for stepper motors but not DC motors. Moreover, variations in supply voltage and coil resistance may influence back EMF values. Another option for current-based obstacle detection systems is the ripple counting method in which ripples are converted into pulses, which are counted and processed to determine the motor's position [6; 7]. [PERSON] used window motor ripple signals for detecting obstacles in power windows (an \"anti-pinch system\"); this system was less expensive than conventional Hall sensors [8]. However, ripple signals have small amplitude and contain noise; hence, in ripple counting, noise must be reduced before features can be detected. [PERSON] et al. [9] proposed a current threshold method for an anti-pinch system to reduce costs compared with light-curtain and sensitive-edge sensor systems; in that method, the current is monitored with a current sensor and the current pattern is analyzed to detect the motor state [10]. This method is widely used and known as Motor Current Signature Analysis. [PERSON] diagnosed the faults in a DC motor based on current signal multi-segment feature extraction [11]. In fault diagnosis systems, a segmentation method is applied with a CNN algorithm to identify the faults [12]. [PERSON] identified the parameters of a DC motor by applying various input voltage signals and analyzing the changes in motor current signal [13]. Obstacles opposing the motor rotation cause abrupt changes in the current signal. [PERSON] et al. [14] developed a technique for automatically detecting abrupt changes in the time-series signals by using a wavelet transform in which a signal is represented as a series of wavelike functions at various frequencies and times. This transform separates the original signal into a time-scale space, and the size of the time and scale (frequency) windows can be adjusted [15; 16]. Hence, changes in a signal wavelet transform can be detected to enable extraction of dominant features from the original signals [17]. Various wavelet transform methods have been employed to monitor motor condition; the most common such method is the discrete wavelet transform (DWT). The DWT is based on signal analysis; it has a small set of scales and a fixed number of translations at each scale. [PERSON] identified changes in motor resistance and friction using wavelet analysis and fuzzy logic [18]. Wavelets generally comes in a family: father and mother wavelets. There are different types of wavelet transforms available to study the signals. One of the criteria to choose the most suitable wavelet and the level of decomposition for our application is considering the highest value of the wavelet coefficient [19; 20]. The coefficient of wavelet transformation represents the relationship between the wavelet function and signal. If the coefficient is high, it is relatively suitable for the raw signal [21]. [PERSON] (1989) introduced wavelet multiresolution analysis [22], a practical version of the DWT in which a single signal is split into a series of small waves from the wavelet family. Thus, while research on obstacle detection exists, it remains niche, with many prior methods lacking compliance with standards like EN14752 [23]. This study provides a novel contribution by addressing these gaps.