# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Case-Based Applications in Structural Health Monitoring and Vibration Diagnostics  
Machine learning techniques have found widespread and impactful applications in structural health monitoring (SHM) and vibration-based diagnostics. These applications often involve large-scale systems, missing data, multisensor integration, and complex time-series behaviors--all of which present challenges well-suited to modern ML models. In [14], a hybrid DCNN-LSTM model was used to predict strain in railway bridge elements subjected to dynamic loads. The approach effectively combined spatial and temporal feature extraction, achieving high accuracy in forecasting stress evolution--a critical need in long-span bridge monitoring. In [29], pretrained CNN models (AlexNet, ResNet) were applied to wavelet scalograms derived from vibration signals, allowing for the accurate detection of damage in bridge structures. The method achieved classification accuracy up to 100%, without requiring manual feature engineering, and proved effective in identifying structural degradation. In [26], a 1D-CNN was employed to detect faults in truss bridges using both simulated and field data. The model achieved an F1-score exceeding 90%, even with single-sensor input, demonstrating that deep learning can provide robust fault detection in sparse-sensor environments. Missing data, a common issue in SHM systems, was addressed in [12] using autoencoders to recover incomplete acoustic emission signals in reinforced concrete monitoring. The reconstruction accuracy exceeded 90%, ensuring continuity in damage evaluation and enhancing long-term monitoring reliability. In [9], vibration, current, and temperature data were combined and analyzed using ML models such as SVM and random forest. The multisensor fusion approach improved fault detection accuracy by over 15% compared to single-sensor systems, illustrating the value of data integration in SHM applications. Tool wear monitoring using acoustic emission was investigated in [37], where CNNs were used to extract time-frequency features, followed by SVM-based classification. The model achieved over 94% accuracy in predicting surface roughness during steel milling, offering an interpretable and high-performance solution. In [13], various ML models (SVM, kNN, decision trees) were compared for diagnosing wind turbine gearbox faults based on vibration analysis. SVM provided the best results, with classification accuracy exceeding 95%, making it suitable for condition-based maintenance strategies. A fuzzy classifier was developed in [23] for identifying hydraulic brake system faults. By using histogram-derived features from vibration signals, the classifier effectively differentiated between fault types, even under overlapping signal conditions. These studies demonstrate the practical effectiveness of both classical and deep learning models in a variety of SHM scenarios--from infrastructure monitoring to rotating machinery diagnostics. The growing diversity of applications confirms that machine learning is not only a promising research direction but also a critical enabler of real-time, autonomous diagnostic systems. The detailed case studies presented in this review underscore machine learning's transformative capacity to address the unique complexities inherent in Structural Health Monitoring (SHM) and vibration-based diagnostics. These applications frequently involve large-scale systems, the challenge of missing or incomplete data, the need for robust multi-sensor integration, and the analysis of intricate temporal behaviors. ML models, particularly deep learning architectures, have proven exceptionally adept at navigating these obstacles. For instance, autoencoders effectively recover missing acoustic emission signals in reinforced concrete, ensuring data continuity for damage evaluation. Similarly, hybrid DCNN-LSTM models accurately predict strain evolution in railway bridges, capturing critical temporal dependencies.