408 results
Showing 1–20 · 0.31 seconds · RSS
International Journal of Prognostics and Health Management
Scopus
ISSN 2153-2648 · The Prognostics and Health Management Society · 408 articles in the index
-
Journal Article
Proficiency of Physics Informed Machine Learning in Multicomponent Fault Recognition of Rotational Machines under Different Speed Conditions
Understanding the limitations of incorporating conventional machine learning synergy led to the inclusion of physics knowledge. This study presents the potency of physics-informed feature engineering for machine learning to enhance fault detection in gears, shafts, and bearings at three…
-
Journal Article
Pump Health Monitoring Test Environment for Diagnosing the Erosive Effects from Cavitation
Cavitation occurs frequently in pumps. The subsequent erosion that is caused by cavitation can significantly reduce the operational efficiency and Remaining Useful Life (RUL) of the pump. This study describes a new hybrid Health Monitoring (HM) test environment, used to diagnose permanent damage…
-
Journal Article
Integrating Machine Learning-Based Remaining Useful Life Predictions with Cost-Optimal Block Replacement for Industrial Maintenance
This study presents a preventive maintenance methodology to predict the remaining useful life (RUL) of mechanical systems and determine cost-effective replacement schedules. The approach integrates machine learning for RUL prediction, Weibull distribution for reliability analysis, and a block…
-
Journal Article
DiffLIME: Enhancing Explainability with a Diffusion-Based LIME Algorithm for Fault Diagnosis
Predictive maintenance, within the field of Prognostics and Health Management (PHM), aims to identify and anticipate potential issues in equipment before they become serious problems. Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN), Long Short-Term Memory (LSTM)…
-
Journal Article
Evaluating the Influence of Time Domain Feature Distributions on Estimating Rolling Bearing Flaking Size with Explainability
To enhance the maintainability of rotating machines, such as wind turbines, where the response to bearing damage is both costly and time-consuming, it is essential to predict the progression of flaking, which is a common rolling bearing fault. Conventional rule-based methods estimate the magnitude…
-
Journal Article
Fault Diagnosis in Non-Stationary Systems via Interval Observers
The paper is devoted to the problem of fault diagnosis in technical systems described by non-stationary linear dynamic equations under unmatched disturbances and measurement noise via interval observers. The problem is to design the observer insensitive to the disturbances and having fewer…
-
Journal Article
Breast Cancer Detection Analysis Using Different Machine Learning Techniques: South Iraq Case Study
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)…
-
Journal Article
Efficiency Monitoring of a Cooling Water Pump based on Machine Learning Techniques
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…
-
Journal Article
A Comparative Analysis of Anomaly Detection Techniques for Battery Telemetry Data in Low Earth Orbit Remote Sensing Satellites
The article presents a comprehensive assessment of telemetry data of batteries used in low-earth orbit satellites. The study further performs an analysis of the performance of using different anomaly detection techniques, including Statistical (Z-Score), Machine Learning (One class support vector…
-
Journal Article
Constraint-Guided Learning of Data-Driven Health Indicator Models: An Application on Bearings
This paper presents a constraint-guided deep learning (DL) framework to develop physically consistent health indicators (HIs) in bearing prognostics and health management. Conventional data-driven approaches often lack physical plausibility, while physics-based models are limited by incomplete…
-
Journal Article
Innovative Distributed Maintenance Concept: From the Design to Cost Optimisation
This study proposes an integrated heuristic framework for the strategic optimization of distributed maintenance operations in geo-distributed production systems (GDPS). It introduces a dual-entity maintenance structure comprising a Centralized Maintenance Workshop (CMW) and a Mobile Maintenance…
-
Journal Article
Analytical Health Indices: Towards Reliability-Informed Deep Learning for PHM
Deep learning has demonstrated significant potential for prognostics in complex systems (Fink et al., 2020). Recent advances in physics-informed machine learning have integrated physics-of-failure principles within data-driven models (AriasChao, Kulkarni, Goebel, & Fink, 2022). Beyond physical…
-
Journal Article
LSTM and Transformers based methods for Remaining Useful Life Prediction considering Censored Data
Predictive maintenance deals with the timely replacement of industrial components relatively to their failure. It allows to prevent shutdowns as in reactive maintenance and reduces the costs compared to preventive maintenance. As a consequence, Remaining Useful Life (RUL) prediction of industrial…
-
Journal Article
Vibration-based Data-driven Fault Diagnosis of Rotating Machines Operating Under Varying Working Conditions: A Review and Bibliometric Analysis
The intelligent fault diagnosis of rotating machines has been significantly advanced by learning-based techniques in recent years. However, the performance of these techniques can drastically decrease under varying working conditions (VWC). This paper investigates the root causes of these decreased…
-
Journal Article
A Comparative Study of Deep Learning Model Based Equipment Fault Diagnosis and Prognosis
Bearing fault diagnosis and prognosis are crucial for the effective management of industrial equipment. Due to the automatic feature extraction of Deep Learning (DL) models, many recent studies have focused on using DL for these tasks. However, most studies address only one of these tasks. This…
-
Journal Article
Comparative Analysis of LSTM Variants for Fault Detection and Classification in Aircraft Control Surfaces
Aircraft control surfaces play a critical role in ensuring safe and efficient flight. Faults in these surfaces could lead to catastrophic consequences. This paper investigates the application of Long Short-Term Memory (LSTM) networks for fault detection and classification in aircraft control…
-
Journal Article
Dynamic Relationship Between Oil Temperature and BGCI in Bell 407 Helicopter: A Cointegration and Autoregressive Distributed Lag Approach
The primary objective of this study was to investigate the dynamic relationship between oil temperature and the Bearing Gearbox Condition Indicator (BGCI) values of the Bell 407 helicopter. The study aims to simplify the fault diagnosis process by proposing a method that utilizes only one vibration…
-
Journal Article
Remaining Useful Life Prediction Using Attention-LSTM Neural Network of Aircraft Engines
Accurate prediction of the Remaining Useful Life (RUL) is essential for the effective implementation of Prognostics and Health Management (PHM) in aerospace, particularly in enhancing aero-engine reliability and forecasting potential failures to reduce maintenance costs and human-related risks. The…
-
Journal Article
Fault-Type-Aware Remaining Useful Life Prediction of Aircraft Engines Using an Integrated Deep Learning Framework
Accurate Remaining Useful Life (RUL) prediction is essential for reducing maintenance costs and improving operational efficiency in high-value, complex systems such as aircraft engines. Data-driven approaches have emerged as a primary methodology in RUL estimation research, demonstrating…
-
Journal Article
Correlation-Enhanced Multi-Scale Residual Network for Bearing Fault Diagnosis in Noisy and Cross-Working Conditions
Bearing fault diagnosis under noisy and cross-working conditions remains a challenging task due to complex signal variations and interference. To address this challenge, this paper proposes a Correlation-Enhanced Multi-Scale Residual Network (CE-MSRN), which effectively captures multi-scale fault…