## 4 Case Study  
The case study is conducted on data collected from an experimental platform built in the Department of Industrial Engineering of the University of Bologna, shown in Figure 6. The case study wants to show the potential of the proposed PHM architecture for industrial equipment, in terms of (1) the ability to recognize setting changes with no training for all possible settings; (2) the ability to detect abrupt faults; (3) the ability to save memory storage space. In other words, the main goal of the case study is to address the requirements from industries listed in Section 3.1, in particular those listed in points 2, 3, and 4, by using the additional functions of the proposed framework listed in Section 3.3. The platform is composed of an asynchronous motor, a gearbox made of two pulleys that exchange the rotation through a belt, two shafts that share the motion thanks to a couple of gears, an electromagnetic brake. The three-phase electric motor has eight poles with power equal to 0.13 kW and rotation speed equal to 660 rpm. The motion is transmitted to the first shaft through the belt running on the pulleys and placed under tension thanks to a screw-system positioned on the motor's support. A steel disk is attached to the second shaft to simulate a load on the centerline using a key and an o-ring. The braking system consists of an electromagnetic dust brake with adjustable braking torque in the range of 0-7.5 Nm and a 90 VDC command control fed by a transformer. The platform contains three accelerometers and a pyrometer. Three Dytran 3093D3 triaxial accelerometers with IEPE technology are placed on the bearing's support, next to the second pulley and the two gearboxes. They have a sampling frequency of 12.8 kHz per axis and an acceleration range of 500 Gpeak. An OPTRIS CSmicro infrared sensor placed near the second pulley measures the pulley or the belt's temperature at a sampling frequency of 1 kHz. Regarding the data collection system, the acceleration sensors are connected to a computer through three-channel NI9230 1/O modules with a maximum sampling rate of 12.8 KS/s for each channel mounted on a four-slot NI 9274 chassis that collects all the data from the accelerometers before sending them to the computer through a USB connection. A data acquisition interface is placed in the pyrometer's cable, and temperature measurements are collected using the plug-n-play software CompactConnect supplied by the Optris company. Figure 6: The test rig (**left**) and its mechanical scheme (**right**). The case study consists of two steps. First, few training data are employed to extract and select relevant features for condition recognition and abrupt fault detection (training). Then, novelty detection and anomaly detection algorithms are tested on streaming data, which were collected under two known operating conditions and two unknown operating conditions, until a sudden fault occurred in the electric motor at the end of the last experiment. Training data consisted of two different operating conditions with different braking torque values (Table 2). The training phase consists of the extraction and selection of the most relevant features for the specific goal, i.e., condition recognition and fault detection. Thus, each vibration signal is divided into segments of 1 s, and, the most typical features in the time domain, i.e., peak, peak-to-peak, mean, RMS, Crest Factor, Skewness, Kurtosis, Shape Factor, and Impulse Factor, are extracted for each segment,. In total, 81 features are extracted, nine for each acquired signal. As an example, the nine features extracted from the first signal (axes x of the first accelerometer) are shown in Figure 7.  
Then, the most relevant features were selected according to the aim:  
1. For the operating condition recognition (OCR) goal, the mean values of the signals 4, 6, and 8, which correspond to the x and z axes of the second accelerometer and the y axes of the third accelerometer, respectively, were selected, as they best distinguish the two settings (Figure 8a). 2.