# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 5 Conclusions  
In this study, we propose a framework for detecting faults in rotating machinery without signals obtained from the rotating machinery in fault states. The main contributions of the proposed framework can be summarized as follows: (1) Owing to the STFT-based spectrogram, information on time-varying patterns of frequency components can be revealed. By doing so, intrinsic properties of noisy and nonstationary signals can be successfully utilized for fault detection on rotating machinery. (2) In addition, by applying the 2D-CAE as a feature extractor, more informative latent features from two-dimensional spectrograms can be extracted in that the convolutional blocks in CAE are specialized to generate features from the image-formatted data. (3) Finally, the OCC algorithm helps to build an elaborate decision boundary under the situations wherein counter classes (i.e., fault signals) are not available, which is frequently encountered in many real industrial fields. In the current study, we first transformed the raw time-domain vibration signal into a spectrogram using the STFT technique in order to represent both time and frequency characteristics. Then, the 2D-CAE was trained to embed the spectrogram into a much smaller latent feature vector. These latent vectors can be obtained by passing through several convolutional blocks within the encoder part of the 2D-CAE, and thus, the encoder of the CAE structures can be used as a feature extractor. Finally, by solely using the extracted latent feature vectors of the normal signals, fault detectors were trained by OCC algorithms, and the fault detector determined whether newly collected signals were generated from a normal state. To demonstrate the efficacy of the proposed fault detection framework, we used the rolling bearing experimental platform, which generates the vibration signals with accelerometer sensors. The comparative experiments demonstrated that our proposed framework achieved the highest AUROC score for bearing fault detection based on vibration signals. In this study, we released whole source codes to implement the experimental studies in the open-source repository ([[https://github.com/BAP-SAE/FD_CAE_Spectrogram](https://github.com/BAP-SAE/FD_CAE_Spectrogram)]([https://github.com/BAP-SAE/FD_CAE_Spectrogram](https://github.com/BAP-SAE/FD_CAE_Spectrogram)); accessed on 6 July 2025). We believe that this uploaded source code can guarantee the reproducibility and replicability of the proposed fault detection framework with the spectrogram and 2D-CAE model. In the future, we will extend the proposed fault detection problems to other rotating machinery systems. Because vibration signals are generally collected from other types of machinery systems, including electric motors and aircraft turbine engines, we believe that the proposed framework can also be applied to fault detection tasks for any rotating machinery systems. Besides, we will apply the proposed framework to the detection of various other fault types. In particular, we will extend the proposed fault detection framework for degradation faults (rust faults, abrasion faults, and scratches), which are not easy to accurately detect. Because of the one-class classification, the proposed framework can successfully detect these types of faults. [PERSON] is responsible for the following parts of the paper: conceptualization, methodology, formal analysis, validation, and writing--original draft preparation, and [PERSON] is responsible for the formal analysis, review and editing, supervision, and fund acquisition. All authors have read and agreed to the published version of the manuscript. The corresponding author ([PERSON]) of this research was supported by the Incheon National University (International Cooperative) Research Grant in 2022. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.