## 1 Introduction
[14] applied a K-nearest neighbor (KNN) method to detect rolling element bearing faults using kurtosis and envelope spectrum analysis. [PERSON] et al. [15] used dimensional analysis and a central composite rotatable design to identify faults in nonlinear rotating systems, focusing on the influence of bearing clearance and external defects on vibration characteristics. A comprehensive study on leak detection and localization in oil and gas pipelines was presented in [16], reviewing various methods, their benefits, limitations, and effectiveness. The authors reviewed different methods, evaluating their advantages, drawbacks, and effectiveness in detecting and pinpointing leaks within pipeline systems. [PERSON] et al. [17] evaluated multiple leak detection methods, discussing their strengths, weaknesses, and areas for future improvement. [PERSON] et al. [18] tackled the issue of the limited historical pipeline failure data in AI methods by introducing a health index approach integrating the Kolmogorov-Smirnov test, multiscale analysis, and a Gaussian mixture model (GMM). In another experiment, [PERSON] et al. [19] classified defects in pipeline welds using the total focusing method (TFM) based on ultrasonic phased arrays, improving defect detection and characterization. They applied the HOG-Poly-SVM algorithm with ten-fold cross-validation for defect classification. In [20], we presented a method for detecting industrial faults using ultrasonic signals by stacking an ensemble of classifiers. [PERSON] et al. [21] presented a technique using ultrasonic guided waves (UGW) with helical wave modes to detect corrosion-type defects in steel pipes. They demonstrated that phase delay differences in wave signal peaks can effectively identify defects with minimal transducers and measurements. [PERSON] et al. [22] presented an experimental study on detecting stress corrosion cracks (SCC) in gas pipelines using guided waves and broadband ultrasound. The study successfully demonstrated the feasibility of an ART-scan-based setup for detecting SCC in a submerged pipe section, showing significant signal differences for cracks with depths of around 35% of the pipe wall thickness. [PERSON] et al. [23] introduced a method combining a fractional Fourier transform (FRFT) and variational modal decomposition (VMD) to detect pipeline defects using ultrasonic signals. The method significantly improved feature extraction and classification accuracy, achieving 89.1% for experimental signals. [PERSON] et al. [6] presented the application of acoustic and ultrasonic methods to detect leaks, blockages, and defects in buried water and sewerage pipes. They reviewed various sensors, including accelerometers, hydrophones, and fiber optics, and explored the potential of autonomous robotics for deploying these sensors. The paper also highlighted data-driven techniques and machine learning for enhancing the accuracy and efficiency of pipe condition assessments. [PERSON] et al. [24] presented a method to carry out pipeline declination inspections using amplitude reduction analysis of ultrasonic echo signals. This method enhanced the detection accuracy, identifying declinations with a maximum error of 0.137\({}^{\circ}\) within a 2\({}^{\circ}\) range. However, these works faced limitations related to environmental influences, computational time, and accuracy. These limitations highlight the ongoing need for advancements in ultrasonic inspection techniques to enhance their reliability and practical applicability in diverse and real-world conditions. This work improves upon the limitations of previous studies by offering a higher accuracy, better computational efficiency, and practical real-time deployment in ultrasonic fault detection for industrial applications. This research covers ten unique fault types in both pipelines and rotating machinery. We compare dimensionality reduction techniques and ensemble classifiers to find the best model for real-time fault detection on an MCU.