Prognostics And Health Management Research Articles

Artificial intelligence-based data-driven prognostics in industry: A survey

In the age of Industry 5.0, prognostics and health management (PHM) is very important for proactive and scheduled maintenance in industrial processes. The target of prognosis is the health state prediction of the system or machine under consideration, hence its Remaining Useful Life RUL. The life of a tool, a part, or a component of the system must be tracked to increase its productivity, reduce human effort and save lives. Data driven prognostics is highly relying on statistical or artificial intelligence AI methods including machine learning (ML) and deep learning (DL) models. AI is a massive enlarging field with encouraging outcomes in prognostics for modelling of data with complex representations and temporal dependencies. A sample of latest research in prognostics especially in industry applications has been collected during this research. About 76% of the collected research papers used data-driven prognostics in their model including 48% applied DL different architectures for prognostic purposes in industrial systems in the last few years. Therefore, this survey concentrate on presenting AI-based data-driven prognostics in industrial systems especially DL-based architectures. The study also puts spot on the main challenges with opportunities of future work in the DL-based PHM applications in the age of Industry 5.0.

Survey of Prognostics and Health Management for Transformers: From Statistical Analysis to Condition-Based Diagnostics

Power transformers are one of the key network components for reliable and efficient operation of power grids. Over the past few decades, there have been growing research efforts in improving the prognostics and health management (PHM) for transformers, including failure analysis using time-to-event data and condition-based diagnostics for both single and multiple components. In this paper, we survey recent literature and relevant works, focusing on widely used statistical models and advanced diagnostic techniques that leverage on condition data and maintenance history. Additionally, we examine the role of artificial intelligence (AI) applications in PHM for power transformers. Finally, we summarize the current limitations and future opportunities to support new research efforts for improving the monitoring of power transformers.

Explainable multimodal learning for predictive maintenance of steam generators

Prognostics and Health Management (PHM) is identified as an important lever for enhancing the development of predictive maintenance to ensure the reliability, availability, and safety of industrial systems. However, the efficiency of data- driven PHM approaches is dependent on the quality and quantity of data. Therefore, exploiting multiple data sources can provide additional, useful information than single-modal data. For instance, by incorporating multiple data sources, including condition monitoring data, images from cameras, and texts from maintenance technicians’ reports, multi-modal learning can provide a more comprehensive and accurate understanding of the system’s health. However, multi-modal deep learning is complex to understand. To address this complexity, it is crucial to incorporate explainable artificial intelligent techniques to provide clear and interpretable insights into how the model makes decisions. In this light, this paper proposes the application of the model-agnostic-explanation approach, i.e., SHAP, to explain the working mechanism of multimodal learning for the prediction of industrial steam generator degradation. Particularly, we determine the important features of each data modality and investigate how multimodal learning can overcome the issues of low-quality data from a single modality due to the additional information from other data modalities.

Building The Health Monitoring and Fault Diagnosis Models For Stamping Press

A stamping press is widely used for the metal forming process. To achieve continuous automation and high precision forming, monitoring the press's health and diagnosing faults during stamping is necessary. The three primary types of faults that may occur in the stamping press are lack of lubrication oil, quality variation of lubrication oil, and clearance variation, which can lead to a decline in workpiece quality and reduced lifespan of the dies and presses. This study adopted the Prognostics and Health Management (PHM) technique to implement a predictive maintenance system for the stamping press. To extract relevant data, the National Instrument (NI) DAQ was used to acquire the three-phase currents and X, Y, and Z vibration signals. Six signals provided a total of seventy-two features, and the top three key features were selected for building a health assessment model using the Logistic regression and PCA algorithms. An early warning is triggered when the health indicator drops below the threshold, alerting the operators. Additionally, fault diagnosis was achieved using classification algorithms such as Support Vector Machine (SVM), K Nearest Neighbors (K-NN), and eXtreme Gradient Boosting (XGBoost). The fault diagnosis model achieved high accuracies of up to 99%.

Development of demonstration system for fault diagnosis of rotating equipments using RK4 test rig

Rotating machinery is an essential equipment in the manufacturing industry, of which the failure can lead to the interruption of whole production line. To prevent such failure, there have been a large number of studies for Prognostics and Health Management (PHM) technology. However, most studies have been focused on a specific algorithm or component, making it difficult to apply them in the field. In this study, a demonstration system for integrated fault diagnosis is developed for critical failure modes in the rotating equipment such as the mass imbalance, shaft misalignment, and the bearing fault using the vibration signals. To this end, Bently Nevada's RK4 rotor kit is revised to impose the failure modes easily. The solution is developed to detect anomalies, identify failure modes, and diagnose them in real time. Coupling ISO-based anomaly detection, rotational failure diagnosis and bearing failure diagnosis, the system is configured to enable comprehensive condition monitoring. The results are demonstrated by real-time simulation of each fault on the test-rig.

PHM for Spacecraft Propulsion Systems: Similarity-Based Model and Physics-Inspired Features

This paper presents a methodology designed for the Prognostics and Health Management (PHM) Asia-Pacific 2023 Conference Data Challenge. In particular, this study targets the health assessment of spacecraft propulsion systems. The challenge involved analyzing and categorizing a simulation-generated dataset that included four unique spacecraft and multiple health conditions, such as normal operation, bubble anomalies, and solenoid valve faults in various system locations. The proposed approach uses a two- step process. First, a model based on similarity measures is employed to classify the data into one of four health states. Then, a model incorporating physics-inspired features is utilized in solenoid valve faults to identify the fault location and estimate the valve opening ratio. The validity of the model is confirmed through cross-validation with the training dataset, which achieved a flawless total score across all permutations. Our method effectively categorizes the test data, including cases from a spacecraft not covered in the training, thereby securing a top position in the competition. The findings highlight the strength of our proposed model, which uses physics-inspired features to predict valve opening ratios, proving useful in managing and interpreting complex, unfamiliar spacecraft health data.

Simple Hybrid Model for Estimating Remaining Useful Life of SiC MOSFETs in Power Cycling Experiments

Recording and prediction of the accumulated damage, which will eventually lead to the failure of power electronic modules, is an aspect of high importance for power electronic systems design and, in particular, for development of Prognostic and Health Management (PHM) schemes for in-field applications. To this end, this paper presents a simple and cost-effective prognostic method for predicting the remaining useful life (RUL) of TO-247 packaged silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs) subjected to power cycling experiments. The model assumes that the major failure mode is bond-wire lift-off and uses a damage accumulation scheme based on Paris’ crack law. The only inputs to the model are historical data on the average junction temperature swing and the temperature-compensated drain-source ON-state resistance at the peak temperature of the current cycle. Using only these two input values, the model is shown to predict RUL with surprising accuracy for the range of constant current loads determining cycling conditions under which the test data series have been acquired. This work is a first step in an ongoing project towards building more elaborate prognostic schemes for RUL-determination of SiC power MOSFETs in actual working conditions, using physics-informed neural networks (PINNs).

Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System

This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %.

To Trust or Not: Towards Efficient Uncertainty Quantification for Stochastic Shapley Explanations

Recently, explainable AI (XAI) techniques have gained traction in the field of prognostics and health management (PHM) to enhance the credibility and trustworthiness of data-driven nonlinear models. Post-hoc model explanations have been popularized via algorithms such as SHapley Additive exPlanations (SHAP), but remain impractical for real-time prognostics applications due to the curse of dimensionality. As an alternative to deterministic approaches, stochastically sampled Shapley-based approximations have computational benefits for explaining model predictions. This paper will introduce and examine a new concept of explanation uncertainty through the lens of uncertainty quantification of stochastic Shapley attribution estimates. The proposed algorithm for estimating Shapley explanation uncertainty is efficiently applied for the 2021 PHM Data Challenge problem. The uncertainty in the derived explanation for a single prediction is also illustrated through personalized prediction recipe plots, improving post-hoc model visualization. Finally, important practical considerations for the implementation of Shapley-based XAI for industrial prognostics are provided.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management (PHM), existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment. A virtual sample generation solution based on Generative Adversarial Network (GAN) is proposed to overcome this shortcoming. Aiming at augmenting the sample classes with the imbalanced data problem, the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models. Under the PHM framework of the on-board train control system, the virtual sample generation principle and the detailed procedures are presented. With the enhanced class-balancing mechanism and the designed sample augmentation logic, the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status. Practical data from a specific type of on-board train control system is employed for the validation of the presented solution. The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance (CBM) operations.

Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.

A novel IoT-based framework with Prognostics and Health Management and short term fire risk assessment in smart firefighting system

In recent years, “smart firefighting” becomes a popular research topic which aims at improving the efficiency of fire protection and safety. However, in existing smart firefighting systems: (1) state and analog firefighting IoT data is not fully utilized; (2) fire risk assessment requires a long time to update; (3) few existing works can recognize faults and forecast potential risks with both temporal and spatial information from multiple IoT sensors. Therefore, a novel framework is proposed to solve the above problems as follows: (1) Short-term Fire Risk Assessment (SFRA) is proposed by considering the working status, maintenance and rectification performance of firefighting facilities based on IoT state data; (2) Prognostics and Health Management (PHM) algorithms are applied in the fire safety field for the first time to detect faults and forecast potential risks with temporal and spatial information with IoT analog data from single or multiple sensors. A case study is carried out in a building in Shanghai and a smart firefighting system is deployed with the edge computing gateway. The experimental results of the SFRA and PHM are presented and analyzed in details. The performance evaluation and comparison results are presented with multiple metrics. The reliability of the proposed framework is discussed. The case study concluded that the proposed framework extends the utilization of IoT data, improves the efficiency of fire risk assessment and enhances the accuracy of faults detection and potential risk forecast. Moreover, the deployed firefighting system improves the data privacy and computational efficiency.

A Reinforcing-Learning-Driven Artificial Bee Colony Algorithm for Scheduling Jobs and Flexible Maintenance under Learning and Deteriorating Effects

In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the best of our knowledge, this paper is the first in the literature to address the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deterioration effects to minimize the makespan. Firstly, we address the PFSP with flexible maintenance and learning effects. Then, the deteriorating effect is also considered. Adaptive artificial bee colony algorithms (ABC) enhanced with Q-learning are proposed, in which the Nawaz–Enscore–Ham (NEH) heuristic and modified NEH heuristics are hybridized with a maintenance insertion heuristic to construct potential integrated initial solutions. Furthermore, a Q-learning (QL)-based neighborhood selection is applied in the employed bees phase to improve the quality of the search space solutions. Computational experiments performed on Taillard’s well-known benchmarks, augmented with both prognostic and health management (PHM) and maintenance data, demonstrate the effectiveness of the proposed QL-driven ABC algorithms.

Remaining useful life prediction of mechanical system based on improved adaptive fractional Lévy stable motion with statistical dependence measurement error

Prediction of remaining useful life (RUL) is a critical component of prognostics and health management (PHM) strategies for mechanical systems. Although some degradation models based on stochastic processes have played a pivotal role in the field of RUL prediction, the diffusion coefficients associated with these models are often fixed values that remain independent of the drift coefficient. Additionally, the measurement error is typically assumed to follow a Gaussian distribution that is independently and identically distributed with respect to the level of degradation. To address these constraints, an RUL prediction framework is proposed based on improved adaptive fractional Lévy stable motion (IAFLSM) with statistical dependence measurement error. The established IAFLSM model is capable of effectively characterizing the individual differences and time-varying uncertainties inherent in equipment degradation processes, with the nonlinear drift and diffusion coefficients exhibiting positive correlation in their variability. Moreover, the state space model is employed to realize the synchronous adaptive dynamic update of the drift coefficient and diffusion coefficient, thus accommodating the mechanical equipment degradation trajectory. Furthermore, a statistical dependence measurement error based on fractional Lévy stable motion is constructed, and the corresponding scale parameter incorporates a dynamic update mechanism with statistical correlation of degradation incremental behavior. The hidden variables related to performance degradation model are estimated through parameter estimation method and characteristic function. Expanding upon the proposed framework for RUL prediction, the quantification of the uncertainty intrinsic in the forecasting results is accomplished by employing the stability theorem of stable distribution and Monte Carlo technique. The RUL prediction framework is validated through the use of authentic truck rear axle full-life data and benchmark rolling bearing data. The comparative analysis results demonstrate the effectiveness and superiority of the proposed methodology.

Physics-informed machine learning in prognostics and health management: State of the art and challenges

Prognostics and health management (PHM) plays a constructive role in the equipment’s entire life health service. It has long benefited from intensive research into physics modeling and machine learning methods. However, in practice, the existing solutions often encounter difficulties caused by sparse data & incomplete system failure knowledge. Pure machine learning or physics-based methods can sometimes be infeasible in such situations. As a result, there has been a growing interest in developing physics-informed machine learning (PIML) models which allow incorporating different forms of physics knowledge at different positions of the machine learning pipeline. This combination provides significant assistance for detection, diagnostic, and prognostics. However, to the best of our knowledge, the bibliometrics analyses and the comprehensive review of the existing research concerning PIML in PHM remain vacant. Our review is therefore dedicated to filling these gaps. We synthesize the concept of PIML in PHM, and propose a taxonomy of PIML approaches from the perspective of “Expression forms of informed knowledge” and “Knowledge informed methods”. The findings and discussions presented in this paper enable us to clarify the current state of the art and the emerging opportunities of PIML approaches, especially for building PHM systems that can work under the “small data and scarce physics knowledge” paradigm.

A cumulative descriptor enhanced ensemble deep neural networks method for remaining useful life prediction of cutting tools

Prognostics and health management (PHM) of turbine cutting tools, particularly the remaining useful life (RUL) prediction is a Gordian technique to maintain the reliability and availability of the turbine, which is also a method to reduce unexpected downtime and total machined costs. Nowadays, most of the recent works in RUL prediction neglect the quality of data used for model training, assuming that the collected sensor data and extracted features can both reflect the degradation process well, which is not always true. This paper firstly introduces a novel cumulative descriptor to better represent the degradation process of cutting tools and further proposes a well-designed stacking-based deep neural network (S-DNN) that contains several different neuron architectures and an ensemble model to construct deep learning layer and ensemble learning layer. Cumulative descriptors are constructed only using directly measured values extracted from raw sensor data, and then the constructed novel features will be fed into S-DNN to extract abstract features from sensor data in multiple ways and automatically learn the relationship between the outputs of deep learning layer and the ground truth by training the ensemble model iteratively. Case studies on experimental data set and NASA Ames milling data set indicate that the proposed method has great effectiveness and generalization in RUL prediction tasks.

Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review

The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies.

A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems

With the advances in artificial intelligence, there is a growing expectation of more automatic and intelligent prognostics and health management (PHM) systems for the real-time monitoring of renewable energy systems. Although the deep learning significantly promotes the development of PHM, it generally works in a close-world assumption that the real-time monitoring data are in-distribution (ID). These methods may lack the ability to alert the system when encountering the out-of-distribution (OOD) data that are previously unseen/unknown. In this study, a unified OOD detection framework is proposed for the intelligent PHM, so as to enhance its reliability and trustworthiness. Specifically, two types of OOD data from unseen working conditions and unseen fault types are comprehensively considered in the unified framework. A class-wise outlier detection strategy is presented to detect the OOD inputs during decision-making. To suppress the unexpected distribution shift caused by variable working conditions, a novel generalization representation of learning towards unseen working conditions is developed by using supervised contrastive learning. The proposed OOD detection framework can not only flag the unreliable diagnostic output of deep learning models, but also reduce the interference of variable working conditions, showing its applicability in real application scenarios. Extensive experiments demonstrate the advantages and the significance of the proposed unified OOD detection framework to establish highly reliable and trustworthy PHM models.

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, health prognosis, and maintenance decision-making. Various software and applications deal with the processes mentioned above independently. We can also observe different development levels, making connecting all of the machine’s technical processes in one health management system with the best possible output a challenging task. This study’s objective was to outline the scope of PHM methods in real-time conditions and propose new directions to develop a decision support tool for marine diesel engines. In this paper, we illustrate PHM processes and the state of the art in the marine industry for each technical process. Then, we review PHM methods and limitations for marine diesel engines. Finally, we analyze future research opportunities for the marine industry and their role in developing systems’ performance and reliability. The main added value of the research is that a research gap was found in this research field, which is that new advanced PHM methods have to be implemented for marine diesel engines. Our suggestions to improve marine diesel engines’ operation and maintenance include implementing advanced PHM methods and utilizing predictive analytics and machine learning.

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance support after a diesel engine failure, but these methods require professional maintenance personnel to carry out manual fault diagnosis, which is time-consuming. Prognostics and health management (PHM), as a new technology in the field of equipment maintenance support, has significant advantages in improving equipment reliability and safety, enhancing equipment maintenance support capability, and reducing maintenance support costs. In view of this, when introducing PHM into diesel engine maintenance support, the research progress and development trend of the key technologies of PHM for diesel engines are carried out with the objective of achieving precise maintenance and scientific management of diesel engines, and the key technologies demand traction. Firstly, the development history of PHM technology is reviewed, and its basic concept and main functions are introduced. Secondly, the system architecture of PHM for diesel engines is constructed, and its key technologies are summarized. Then, the research progress in the field of PHM for diesel engines is reviewed from four aspects: data acquisition, data processing, fault diagnosis, and health status assessment. Finally, the challenges faced by diesel engine PHM in engineering applications are analyzed, effective solutions to address these challenges are explored, and the future development trend is foreseen.