# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 4 Simulation Results  
No-obstacle, soft-object, and hard-object detection performance was evaluated in a simulation. The safety standard EN14752 specifies that the system should be sensitive to forces of 100 N and that the peak force on the object should not exceed 400 N. Therefore, in the proposed system, forces above the sensitivity level (100 N) were applied. In addition, control action should be taken in less than 1 s. Within this time frame, soft objects experience lower forces (due to the pinching effect), whereas hard objects experience higher forces. Consequently, forces in the range of 100 N to 150 N were assumed to correspond to soft objects, and forces above 150 N were assumed to correspond to hard objects. Therefore, to simulate soft and hard objects, any forces between the above assumed ranges could be applied to the simulation model. In this case, as an example, forces of 105 N and 182 N were applied. A load force signal was applied from 3 to 4 s to mimic an obstruction of the door movement. The MATLAB function output a corresponding result if the W9 and peak signals matched any of the rules. In all cases, a 0.15 Nm torque command was used. The cases were as follows. For the no-obstacle case, no load force signal was applied. Figure 10a reveals that W9 and peak were high at the door start and stop positions (0 and 11.1 s) due to the motor starting current and regenerative braking, respectively. While the door was closing, the amplitudes of W9 and peak decreased to 22.8 and 5.46, respectively, which satisfied the rule for the no-obstacle condition. Hence, the obstacle detection algorithm displayed the output message \"no obstacle\" [Figure 10b]. Figure 10c reveals that the steady-state current while the door was closing was 1.3 A due to dynamic friction. After the door reached the closed position, the motor was turned off, triggering regenerative braking, which caused a negative spike in the current signal. All of these changes in the current signal were reflected in the W9 and peak. Figure 10: No-obstacle scenario. (**a**) W9 and peak amplitudes; (**b**) simulation output; (**c**) motor current signal. A soft-object obstruction was simulated by applying a 105 N load force signal at exactly 3 s. The MATLAB function was programmed to stop the simulation when it detected a soft object. In Figure 11a, the amplitudes of W9 and peak indicate that the motor started and the door displaced forward without any obstacles up to the 2 s time point. The applied load force obstructed the door movement at 3 s, which caused the motor current to increase to 2.35 A [Figure 11c]. This change in the current was detected by the algorithm because the amplitude of W9 and peak increased to 40.79 and 9.91, respectively [Figure 11a], crossing the soft-object detection threshold; hence, the simulation was stopped. The output of the obstacle detection function displayed \"no obstacle\" until 2 s and \"soft object detected\" at 3 s [Figure 11b]. Finally, a hard-object obstruction was simulated by applying a load force signal (182 N) higher than the soft object threshold (150 N) at 3 s. The MATLAB function stopped the simulation when it detected a hard object. In Figure 12a, the amplitudes of W9 and peak were below the obstacle threshold level until 2 s. At 3 s, the applied load force caused the motor current to increase to 3.13 A [Figure 12c]. The obstacle detection function detected this change; the amplitude of W9 and peak increased to 53.74 and 13.17, respectively [Figure 12a], passing the hard-object detection threshold, and the simulation was stopped. The simulation output displayed the \"no obstacle\" message until 2 s and \"hard object detected\" at 3 s [Figure 12b]. As for the sensitivity of the algorithm to small obstructions or objects with irregular shapes, the algorithm is sensitive only to forces above 100 N (according to EN14752). It cannot detect obstruction forces below 100 N. Small or irregularly shaped objects may generate lower or inconsistent signal amplitudes, which could challenge the detection algorithm.