This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques.

Gears are widely recognized as crucial components for power connections and transmission owing to their substantial loading capacity, transmission efficiency, and consistent power output. Given their complexity, they are prone to faults and require continuous condition monitoring (CM). The study proposes an innovative approach that combines signal analysis with data-driven techniques to monitor gear conditions accurately. By employing a shallow neural network (NN) model, which incorporates statistical time features and principal component analysis (PCA), this strategy achieves precise detection of seven distinct levels of gear wear. The feature generation technique utilizes a sliding and overlapping window to extract 15 statistical time features from the tri-axial vibration signals of the gearbox. The PCA has been applied to the feature set (FS) to retain relevant features, avoid overfitting during model training, and visualize the underlying structure of the training data. A comparative analysis with other leading classification methods, applied to both the original and PCA-reduced feature sets reveals that the shallow NN model, particularly when using the PCA-reduced FS, stands out for its exceptional performance. It achieves a remarkable test accuracy of 99.1% on experimental data having a linear time complexity, highlighting the model’s practicality for real-world applications.

Broken rotor bar faults (BRBFs) in induction motors (IMs) present significant challenges in industrial applications, particularly due to the need for large labeled datasets and fast processing. This study addresses these issues by leveraging transfer learning with classical diagnostic techniques, using experimental 3-phase current and 3-axes vibration signals. The Gramian Angular Field (GAF) technique has been utilized to transform time series data into 2D images, enabling fine-tuning of an EfficientNetB3 model, which achieved 99.83% accuracy in classifying five BRBF severity levels. The proposed strategy also outperforms the state-of-the-art methods using the same experimental data. Similarly, validation with features extracted using Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) further confirmed its reliability and superiority. This study also offers enhanced interpretability through Grad-CAM visualizations of the best model, which highlights the critical regions contributing to fault classification. These visualizations enable deeper and simpler understanding of fault mechanisms and support subsequent risk analysis, making the developed model actionable and user-friendly for industrial applications.

This study presents a proof-of-concept investigation into the use of proximal policy optimization (PPO), a deep reinforcement learning (DRL) algorithm, for estimating the remaining useful life (RUL) of rolling element bearings. Although DRL has shown growing promise in prognostics, existing applications have predominantly relied on off-policy deterministic actor–critic methods such as deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3); the suitability of on-policy clipped-objective methods such as PPO for this task remains comparatively unexplored. To address this gap, statistical time-domain features are extracted from raw vibration signals and used as input to train a PPO agent with an actor–critic architecture, in which the actor network predicts RUL values and the critic network evaluates prediction quality through state-value estimation. A preprocessing pipeline comprising feature extraction, normalization, and sliding-window segmentation is developed, and the PPO framework incorporates generalized advantage estimation (GAE), a custom-designed reward function, and a policy-clipping mechanism to support stable training. The method is evaluated on a representative bearing (Bearing 2_1) from the XJTU-SY run-to-failure dataset using a chronological train/test split, and benchmarked against long short-term memory (LSTM) networks, multilayer perceptrons (MLPs), and a naive linear regression baseline. Performance is assessed using root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), and a domain-specific asymmetric scoring function that penalizes late predictions more heavily than early ones. Experimental results show that the PPO-based model produces more stable and operationally favourable RUL estimates than the supervised baselines on the unseen late-degradation segment, particularly in the critical end-of-life region. The findings support PPO as a viable on-policy DRL formulation for bearing RUL prediction and motivate further validation across multiple bearings and operating conditions, identified here as essential future work.

Around 1 year of measured data for Alghero, Italy was used to train and test two machine learning algorithms namely the Random Forests method and the Multi-Layer Perceptron (MLP) with the Random Forest being the best. The input predictors were the wind velocity, wave peak period and the significant wave height (SWH). These were normalized before being fed to the model. The output response was significant wave height. It is difficult to select wave energy extraction sites therefore a better understanding of significant wave height is needed through accurate prediction. This work explores the possibilities of exploring and comparing machine learning algorithms which could forecast SWH accurately since it is the most important parameter used in studying the energy output of wave energy converters.