International Journal of Prognostics and Health Management

Efficiency Monitoring of a Cooling Water Pump based on Machine Learning Techniques

Marta Casero — 2025-01-22

This paper presents a method for efficiency monitoring of two circulating water pumps working in a combined cycle power plant for cooling the steam coming from a water-steam turbine. The method is based on monitoring the performance of the pumps over time using machine learning techniques that try to discover patterns in the data observed from the pumps. This permits the maintenance staff to assess the possible degradation of the pumps and evaluate the effect of the corrective and preventive maintenance implemented. Some examples of real cases will be presented in the paper to illustrate the method proposed.

Breast Cancer Detection Analysis Using Different Machine Learning Techniques: South Iraq Case Study

Salma Abdulbaki Mahmood — 2025-02-26

Contemporary oncology has seen a growing interest in digital technologies, whose integration with extensive healthcare and clinical data has raised new aspirations in managing patient profiles and organizing treatment plans. Among the commonly used digital technologies are Machine Learning (ML) methods that can perform many tasks, such as prediction, classification, and description, based on previously stored big data with high precision and speed. This study aims to develop a predictive ML model for early prediction of breast cancer based on a set of medically categorized risk factors. The locally collected database contained 415 instances from Al-Sadr Teaching Hospital in Basrah, Iraq, 219 (53%) of which were breast cancer patients, whereas 196 (47%) of them were control, respectively non-patients. It trained seven machine learning methods, namely Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logical Regression (LR), Multinominal Naïve Bayes (NB), and Gaussian NB. The dataset was cleaned and balanced before being used. The results proved the superiority of the Decision Tree model with 96% accuracy, 96% sensitivity, and 96% specificity, the Random Forest model with 94% accuracy, 100% sensitivity, and 87% specificity, and SVM model with 92% accuracy, 96% sensitivity, and 87% specificity, respectively. Other models gave diverging results. The current study concluded that modern technologies should be employed to raise awareness and control diseases. The need to adopt Electronic Health Records (EHR) to ensure the integration of clinical data of different types recorded over time for patients contributes to building accurate and reliable prediction models.

Uncertainty Assessment Framework for IGBT Lifetime Models. A Case Study of Solder-Free Modules

Ander Zubizarreta — 2024-12-30

Insulated gate bipolar transistors (IGBTs) are ubiquitous semiconductor devices used in diverse electronic power applications. The reliability and lifetime assessment of IGBTs is intricate and influenced by different ageing processes. One of the main ageing mechanisms is the bond wire lift-off failure mode. The model used to describe this failure mode and estimate the IGBT lifetime is influenced by different variables and factors, which are stochastic, and tend to be specifically adjusted for different IGBT modules and applications. However, unless these variables are not assessed with respect to potential sources of uncertainty, the IGBT lifetime estimate leads to a single-value deterministic estimate, which, frequently, results inaccurate. In this context, assessing the influence of the variability of these variables on the lifetime model is a crucial activity for an uncertainty-aware IGBT lifetime estimate and adoption of appropriate sensing technology. Accordingly, this paper presents a methodology to evaluate the impact of the uncertainty of IGBT lifetime parameters on the lifetime estimate. The approach is first validated on three different experimental IGBT operation profiles, demonstrating the impact of variations of certain variables on the damage estimation. The approach has been tested here for a single lifetime model, but it is generally applicable to other IGBT lifetime models.

Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings

Boubker Najdi — 2024-12-30

Predicting the Remaining Useful Lifetime (RUL) of bearings is crucial for the maintenance and reliability of rotating machinery. This paper presents a novel approach utilizing PRONOSTIA and XJTU-SY datasets for RUL prediction. The proposed methodology leverages Synchrosqueezing Wavelet Transform (SSWT) and Random Projection (RP) to extract significant features from vibration signals. These features are then fed into a Residual Network (ResNet) combined with a temporal attention layer, followed by a Long Short-Term Memory (LSTM) model, referred to as the Adaptive Residual Attention LSTM (ARAL), to assess the Health Indicator (HI) of the bearings. Notably, an exponential data labeling technique is employed instead of traditional linear labeling, enhancing the robustness of the HI assessment. Following the HI estimation, the three-sigma method is applied to identify the degradation starting point. Subsequently, Gaussian Process Regression (GPR) is utilized to predict the RUL from this point forward. The proposed method demonstrates superior performance compared to existing techniques, providing more accurate and reliable RUL predictions. Experimental results show that this integrated approach effectively captures the complex degradation patterns of bearings, making it a valuable tool for prognostics and health management in industrial applications.

Robust Kalman Filter with Recursive Measurement Noise Covariance Estimation Against Measurement Faults

Chingiz Hajiyev — 2025-01-25

A new innovation-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed innovation-based RKF with recursive estimation of measurement noise covariance is applied for the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real time in the presence of various types of sensor faults. Estimation accuracies of the proposed RKF and conventional KF are investigated and compared. In all investigated sensor fault sceneries, the Root Mean Square (RMS) errors of the proposed RKF estimates are lower. The conventional KF gives inaccurate estimation results in the presence of sensor faults.

Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning

Achmad Widodo — 2025-01-31

This paper reports diagnostics and prognostics study of boiler in power plant using actual boiler operating data. This study aims to early detect anomalies that occur in the boiler and to predict the remaining useful life (RUL) after anomalies are detected. The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. The developed method is validated by testing the prediction models using real operating data acquired from three boilers in power plant. The results show that some anomalies are successfully detected by prediction model even though there are anomalies that give low accuracies in predictions. RUL prediction also provides fair results given the limitations of the real data used in building prediction models. Overall, the results of this study have potential to be applied in real system as an auxiliary tool in the boiler condition monitoring to support boiler maintenance programs.