Search Results (12)

Power transformers are critical assets in modern power grids, where failures can lead to significant operational disruptions and financial losses. Dissolved Gas Analysis (DGA) is a key sensor-based technique widely used for condition monitoring, but traditional diagnostic approaches rely on deterministic thresholds that overlook uncertainty in degradation dynamics. This paper proposes a probabilistic framework for Prognostics and Health Management (PHM) of power transformers, integrating self-adaptive Auto Regressive Integrated Moving Average modeling with a probabilistic reformulation of Duval’s graphical methods. The framework enables automated estimation of fault types and failure likelihood directly from DGA sensor data, without requiring labeled datasets or expert-defined rules. Dissolved gas dynamics are forecasted using time-series models with residual-based uncertainty quantification, allowing probabilistic fault inference from predicted gas trends without assuming deterministic persistence of a specific fault type. A sequential pipeline is developed for real-time fault tracking and reliability assessment, aligned with IEC, IEEE, and CIGRE standards. Two case studies validate the method: one involving gas loss in an experimental setup and another examining thermal degradation in a 345 kV transformer. Results show that the framework improves diagnostic reliability, supports early fault detection, and enhances predictive maintenance strategies. By combining probabilistic modeling, time-series forecasting, and sensor-based diagnostic inference, this work contributes a practical and interpretable PHM solution for sensor-enabled monitoring environments in modern power grids. Full article

Remaining useful life (RUL) estimation of a bearing is a methodology to monitor rolling bearings for a system’s performance and reliability. It predicts the exact residual time without operational interruptions until complete bearing failure by training a deep learning model to predict the remaining time of working using extracted signal features. Extracting features is one of the most important subjects since its quality directly influences the performance of predicting RUL. Features should gradually and consistently increase over time and capture sudden deterioration within normalized specific thresholds. However, recent studies have not addressed feature extraction methods that consider all of these aspects. Moreover, some bearings exhibit a “self-healing” phenomenon, in which bearing conditions appear to temporarily improve, and this complicates the accurate representation of consistent performance degradation. However, very few studies have properly addressed this issue. Meanwhile, transfer learning is frequently used when training the RUL deep learning model because there is a lack of data for run-to-failure experiments. Most RUL estimation methodologies pre-train and apply deep learning models with supervised learning. But supervised transfer learning supposes that researchers already have access to end-of-life (EOL) data—often unavailable in industrial settings—limiting their practicality. To address these challenges, this paper proposes a novel semi-supervised transfer learning methodology that integrates an anti-self-healing health indicator (ASH-HI) with a transformer-based architecture. ASH-HI is a health indicator that quantifies the power spectrum density (PSD) difference between normal and abnormal states using skewness-based parameter selection, eliminating the need for manual parameter tuning. Also, it overcomes the self-healing problem by measuring the difference not only between normal and abnormal states but also between “correction” and abnormal states. Also, this paper presents a new semi-supervised transfer learning method without EOL information. The proposed methodology is validated using the PHM 2012, NASA IMS, and an experimental setup. This study is the first to attempt transfer learning using more than three datasets simultaneously, resulting in significantly improved performance. Full article

Power transformers (PTs) play a vital role in the electrical power system. Assessing their health to predict their remaining useful life is essential to optimise maintenance. Scheduling the right maintenance for the right equipment at the right time is the ultimate goal of any power system utility. Optimal maintenance has a number of benefits: human and social, by limiting sudden service interruptions, and economic, due to the direct and indirect costs of unscheduled downtime. PT now produces large amounts of easily accessible data due to the increasing use of IoT, sensors, and connectivity between physical assets. As a result, power transformer prognostics and health management (PT-PHM) methods are increasingly moving towards artificial intelligence (AI) techniques, with several hundreds of scientific papers published on the topic of PT-PHM using AI techniques. On the other hand, the world of AI is undergoing a new evolution towards a third generation of AI models: large-scale foundation models. What is the current state of research in PT-PHM? What are the trends and challenges in AI and where do we need to go for power transformer prognostics and health management? This paper provides a comprehensive review of the state of the art in PT-PHM by analysing more than 200 papers, mostly published in scientific journals. Some elements to guide PT-PHM research are given at the end of the document. Full article

In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field. Full article

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment’s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark. Full article

In today’s industrial environment, effectively monitoring assets throughout their entire lifetime is essential. Prognostic and Health Management (PHM) is a powerful tool that enables users to achieve this goal. Recently, sensor-equipped actuator electrification has been introduced to capture intrinsic key system variables as time series. This data flow has opened up new possibilities for extracting essential maintenance information. To leverage the full potential of these data, we have developed a novel algorithm for time series registration, which serves as the core of a new similarity-based prognostic method in a PHM context: Partial Time Scaling Invariant Temporal Alignment for Remaining Useful Life Estimation (PARTITA-RULE). Our algorithm transforms acceleration signals into a subset of descriptors for a new actuator, creating a time series. We can extract valuable maintenance information by aligning this time series with the one already labeled from past behaviors of the same actuator’s family of heterogeneous sizes and robust scaling factors. The unique aspect of our method is that we do not need to inject prior knowledge for registration intervals at this stage. Once the unknown series is aligned with all possible candidates, we create a weighting scheme to assign a relevance score with an uncertainty measurement for each aligned pair. Finally, we compute interpolants on the Wasserstein space to obtain the asset’s Remaining Useful Life (RUL). It is important to note that a relevant result in a PHM context requires a database filled with different labeled system behaviors. To test the effectiveness of our method, we use an industrial data set of vibration signals captured on an aeronautical electric actuator. Our method shows promising Remaining Useful Life (RUL) estimation results even with incomplete time segments. Full article

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning. Full article

This paper focuses on the prognosis problem in manufacturing of the electronic chips for devices. Electronic devices are of great importance at present, which are popularly applied in daily life. The basis of supporting the electronic device is the powerful electronic chip and its manufacturing technology. Chip manufacturing has been one of the most important technologies in recent years. The etching machine is the key equipment in the etching process of the wafers in chip manufacturing. Due to the high demands for precise manufacturing, monitoring the health state and predicting the remaining useful life (RUL) of the etching system is quite important. However, the task is very hard because of the lack of knowledge of exact onset of failure or degradation and the multiple operating conditions, etc. This paper proposes a novel deep learning-based RUL prediction method for the etching system. The transformer module and random forest are integrated in the methodology to identify the health state of the machine and predict its RUL, through training with the complex data of the etching machine’s sensors and exploring its underlying features. The experiments are based on the subject of the 2018 PHM Data Challenge—for estimating time-to-failure or RUL of Ion Mill Etching Systems in an online fashion using data from multiple sensors. The results indicate the proposed method is promising for the real applications of the prognosis of the etching system for electronic devices. Full article

Prognostics and health management (PHM) are gradually being applied to production management processes as industrial production is gradually undergoing a transformation, turning into intelligent production and leading to increased demands on the reliability of industrial equipment. Remaining useful life (RUL) prediction plays a pivotal role in this process. Accurate prediction results can effectively provide information about the condition of the equipment on which intelligent maintenance can be based, with many methods applied to this task. However, the current problems of inadequate feature extraction and poor correlation between prediction results and data still affect the prediction accuracy. To overcome these obstacles, we constructed a new fusion model that extracts data features based on a broad learning system (BLS) and embeds long short-term memory (LSTM) to process time-series information, named as the B-LSTM. First, the LSTM controls the transmission of information from the data to the gate mechanism, and the retained information generates the mapped features and forms the feature nodes. Then, the random feature nodes are supplemented by an activation function that generates enhancement nodes with greater expressive power, increasing the nonlinear factor in the network, and eventually the feature nodes and enhancement nodes are jointly connected to the output layer. The B-LSTM was experimentally used with the C-MAPSS dataset and the results of comparison with several mainstream methods showed that the new model achieved significant improvements. Full article

The burgeoning prognostic and health management (PHM) engineering technology with superior performance has lately received extensive attention in the academic circle. Nevertheless, the various types of faults of the power transformer often lead to less accurate predictions and the instability of the power system. To address these problems, a power transformer PHM model with a hybrid machine learning method-approach is proposed in this paper. The model uses intelligent sensors to obtain dissolved gas analysis (DGA) data for fault diagnosis of the power transformer system, so as to compress the complexity of features (gas types) in the power transformer. In particular, to enhance the robustness of the model, we adopt a modified differential evolution whale optimization algorithm (MDE-WOA) to optimize the probabilistic neural network (PNN), namely, the classification performance of the model is improved by updating the smoothing factor ( $\sigma$ ) of PNN. In addition, compared with other optimization algorithms, the MDE-WOA algorithm has a lower complexity and more stable optimization process. Finally, we evaluate this model with real world data from the power transformer sensor in Jiangxi province, China. The results indicated that the proposed algorithm could achieve the highest diagnostic accuracy in the fourth iteration, its accuracy having reached 98.86%. Therefore, the proposed PNN parameter optimization meta heuristic algorithm could effectively enhance the accuracy and efficiency of the power transformer fault diagnosis. Full article

Prognostics and Health Management (PHM) is an emerging technique which can improve the availability and efficiency of equipment. A series of related optimization of the PHM system has been achieved due to the growing need for lowering the cost of maintenance. The PHM system highly relies on data collected from its components. Based on the theory of machine learning, this paper proposes a bio-inspired PHM model based on a dissolved gas-in-oil dataset (DGA) to diagnose faults of transformes in power grids. Specifically, this model applies Bat algorithm (BA), a metaheuristic population-based algorithm, to optimize the structure of the Back-propagation neural network (BPNN). Furthermore, this paper proposes a modified Bat algorithm (MBA); here the chaos strategy is utilized to improve the random initialization process of BA in order to avoid falling into local optima. To prove that the proposed PHM model has better fault diagnostic performance than others, fitness and mean squared error (MSE) of Bat-BPNN are set as reference amounts to compare with other power grid PHM approaches including BPNN, Particle swarm optimization (PSO)-BPNN, as well as Genetic algorithm (GA)-BPNN. The experimental results show that the BA-BPNN model has increased the fault diagnosis accuracy from 77.14% to 97.14%, which is higher than other power transformer PHM models. Full article

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM. Full article