# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 4 Experimental Study  
In this study, we collected vibration signals from our own rolling-element bearing experimental platform presented in Figure 3.  
In this bearing experimental platform, the vibration signals were collected from the accelerometer sensor with a sampling frequency of 10,240 Hz, and the accelerometer sensor is placed on the topside of the bearing housing. In this study, vibration signals were collected by the KS94C100 accelerometer sensor, a miniature piezoelectric vibration sensor manufactured by MMF (Located in Meissner, Germany). For precise vibration measurements, it generates an IEPE (Integrated Electronics Piezo-Electric) output with a sensitivity of 10 mV/g (\(\pm\)5%). The measurement range of the KS94C100 is \(\pm\)60 g, corresponding to  
Figure 3: Our own rolling-element bearing experimental platform. approximately \(\pm\)600 m/s\({}^{2}\), and it can capture both low- and high-acceleration signals. Its frequency sampling ranges from 0.3 Hz to 28 kHz (\(\pm\)3 dB), ensuring accurate collection of broadband vibration signals, including high-frequency components. In the current study, a signal is collected with a length of 10,240 sampling points (i.e., a raw signal is collected per unit second). In addition, the rotating speed of the bearing is 1200 rpm (revolutions per minute), and the vertical loader presses a bearing housing with 150 kgf (kilogram-force). For the experimental platform, we used a deep-groove ball bearing of type 6207 DD NSK (Manufactured from Tokyo, Japan), whose sizes of bore diameter, outside diameter, and width are 35 mm, 72 mm, and 17 mm, respectively. The signals are collected not only from a normal bearing but also from bearings having different faults which have occurred in the ball element, inner race, or outer race. These fault bearings are shown in Figure 4.  
A number of previous studies regarding FDD for the bearings have also considered these three fault types because they are the most representative fault types [17; 71]. Each fault at three different positions of the bearing was generated through electrical discharge machining (EDM). These three fault types are shown in Figure 4, where the fault locations are indicated by the bolded boxes. Regardless, it is known that the vibration patterns vary with fault intensity levels, and thus we also considered two fault intensity levels (i.e., strong and weak) by different defect diameters for each fault type. Consequently, the bearing state types considered in the current study are as follows: normal condition (N), strong ball element fault (SB), weak ball element fault (WB), strong inner race fault (SI), weak inner race fault (WI), strong outer race fault (SO), and weak outer race fault (WO). To conduct all experiments, we utilized an Intel i5-9400F CPU (Intel, Santa Clara, CA, USA), NVIDIA GeForce RTX 2060 GPU (NVIDIA, Santa Clara, CA, USA), and 16 GB of RAM. All the DNN-based methods were implemented with the GPU-accelerated _Pytorch_ (_version 1.12.1_) in _Python_ (_version 3.9.13_). We also used the _Scikit-learn_ library (_version 1.1.1_) to implement OCC algorithms. Finally, we implemented the STFT procedure using the _np.fft_ function in the _numpy_ library of _Python_ (_version 3.9.13_). As mentioned earlier, the training dataset consists of only normal signals, and the testing dataset is composed of the signals both in the normal operating condition and for all fault types. The number of samples for seven different bearing states is presented in Table 2. One sample corresponds to a signal collected per second, having a length of 10,240 data points. Please note that each method is tested over 10 repetitions in order to reduce the effect of random errors. Please note that, in this study, we did not partition the training dataset as training and validation sets because the proposed framework is basically an unsupervised learning setting.