# Artificial Intelligence in Fault Diagnosis and Signal Processing
## 2 An Overview of Published Articles  
This Special Issue compiles 15 research works and one review focusing on artificial intelligence and signal processing techniques applied to fault diagnosis in different industrial problems and environments.  
In these works, different signals are acquired and processed. Signals extracted from most of the most widely used sensors are considered: linear strain gauges, temperature sensors, vibration and acoustic sensors, among others. These signals address a variety of real-world problems, such as data imbalance, management of missing data and outliers, data augmentation, pseudo-anomaly sample generation, and the creation of benchmark datasets.  
In addition, different signal processing techniques and artificial intelligence techniques have been proposed as the most suitable solutions for modeling and extracting information from signals of different natures. Most systems combine time-series data analysis methods for preprocessing with supervised or unsupervised learning methods for system prediction. Some examples of the time-series data analysis methods for preprocessing proposed in these works are cluster-based segmentation or Mel-frequency cepstral coefficients, discrete wavelet transform, and hybridization of principal component analysis and kernel regression methods based on mutual information. The learning methods for system prediction proposed in these works are cutting-edge solutions, such as convolutional neural networks, extreme learning machine classifiers, the YOLOv8 deep neural network, deep support vector data description models, and one-dimensional convolutional neural networks.  
Finally, this combination of artificial intelligence and signal processing techniques has been applied to different fault diagnosis cases. Some of these cases are related to high-end industries, such as the fault detection of next-generation chemical reaction devices, temperature-based warnings in high-speed trains, or defect detection in the manufacturing of laminated composites or printed circuit boards. Some others are focused on well-spread moving devices with a high impact on our society, such as real-time monitoring of conveyor belt damage, engine fault detection of traditional vehicles, fault detection of gears in machinery systems, or positional accuracy degradation in industrial robots. Lastly, others are focused on mechanically fixed structures, such as superficial defect detection for concrete bridges or submerged structure-foundation systems. In this way, this Special Issue covers a wide range of highly demanding applications.  
To support this descriptive analysis, a bibliometric analysis was conducted on the 16 manuscripts included in this Special Issue. Figure 1 shows a cloud graph of the words included in the keywords of these works. Firstly, this figure outlines the orientation of all works with the Special Issue topic, being the words machine learning and those words related to fault diagnosis (predictive maintenance, faults diagnosis, and detection) the most relevant. Secondly, it includes words related to the most promising machine learning techniques for these purposes, such as deep learning, convolutional neural networks, or adversarial learning. Thirdly, it includes words about the main signals to be taken into consideration in this field, such as temperature, vibration, or acoustic. Fourthly, it includes words that highlight the main challenges of machine learning in this field of application, such as data imbalance, benchmark datasets, or cluster detection. Finally, it includeswords related to the final application of these techniques to this research field, such as belt conveyors, circuit boards, or failure analysis. In summary, Figure 1 shows the convergence of published keywords in the works included in the Special Issue, by means of using different machine learning techniques and sensors' signals in different industrial problems but facing the same challenges and finding generalizable solutions.