Prognostic Framework Research Articles

Developing Computational Intelligence for Smart Qualification Testing of Electronic Products

In electronics manufacturing, the necessary quality of electronic components and parts is ensured through qualification testing using standards and user requirements. The challenge is that product qualification testing is time-consuming and comes at a substantial cost. The work contributes to develop a novel prognostics framework for predicting qualification test outcomes of electronic components enabling the reduction of qualification test time and cost. The research focuses on the development of a new, prognostics-based approach to qualification of electronics parts that can enable “smart testing” using data-driven modelling techniques in order to ensure product robustness and reliability in operation. This work is both novel and original because at present such approach to qualification testing and the associated capability for test time reduction (respectively cost reduction) it offers are non-existent in the electronics industry. An effective way of using three different methods for development of prognostics models are identified and applied. Predictive models are constructed from historical qualification test data in the form of electrical parameter measurements using Machine Learning (ML) techniques. ML models can be imbedded within the sequential electrical tests qualification procedure and enable the forecasting of the pass/fail qualification outcome using only partial information from already completed electrical tests. Data-driven prognostics models are developed using the following machine learning techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN) and (3) K-Nearest Neighbor (KNN). The results show that with just over half of the individual tests completed, the models are capable of forecasting the final qualification outcome, pass or fail, with accuracy as high as 92.5%. The predictive power and overall performance of the researched models in predicting qualification test binary outcomes with varying ratios of Pass and Fail data in the processed datasets are analysed.

Model-based vehicular prognostics framework using Big Data architecture

Nowadays, the continuous technological advances allow designing novel Integrated Vehicle Health Management (IVHM) systems to deal with strict safety regulations in the automotive field with the aim at improving efficiency and reliability of automotive components. However, challenging issue, which arises in this domain, is handling a huge amount of data that are useful for prognostic. To this aim, in this paper we propose a cloud-based infrastructure, namely Automotive predicTOr Maintenance In Cloud (ATOMIC), for prognostic analysis that leverages Big Data technologies and mathematical models of both nominal and faulty behaviour of the automotive components to estimate on-line the End-Of-Life (EOL) and Remaining Useful Life (EUL) indicators for the automotive systems under investigation. A case study based on the Delphi DFG1596 fuel pump has been presented to evaluate the proposed prognostic method. Finally, we perform a benchmark analysis of the deployment configurations of ATOMIC architecture in terms of scalability and cost.

A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory

In recent years, hybrid energy storage systems have been widely used in electric vehicle and smart grid applications. Real-time and robust modeling and state estimation are essential to the reliable and safe operation of the hybrid energy storage system. Although there exists a considerable mass of research on the modeling and state estimation of the lithium-ion batteries, a survey focusing on the remaining discharge time prognostic for the hybrid power source system has not been conducted. To fill this gap, this paper handles the problem of fractional-order modeling and the remaining discharge time prognostic of the lithium-ion battery and ultra-capacitor hybrid energy storage system. First, the fractional-order models for the lithium-ion batteries and ultra-capacitors are presented, where the particle swarm optimization Algorithm with the Chaos theory is employed for parameter identification in the time domain. Second, a Markov load trajectory prediction is proposed for enhancing the reliability and robustness of the remaining discharge time prognostic. Third, the prognostic framework of the hybrid energy storage system is presented based on the Bayesian method. The results with urban dynamometer driving schedule are analyzed and discussed, which indicate that the proposed method has high accuracy and robustness.

Health-Conscious Energy Management for Fuel Cell Hybrid Electric Vehicles Based on Prognostics-Enabled Decision-Making

In this paper, a health-conscious energy management strategy based on prognostics-enable decision-making in the framework of prognostics and health management has been proposed for the studied fuel cell hybrid electric vehicle. Fuzzy logic controllers have been optimized by off-line genetic algorithm under different degradation states and corresponding confidence factors have been allocated to them according to the on-line health state classification results. Then, the parameters of the off-line tuned fuzzy logic controllers are fused together based on Dempster-Shafer data fusion theory in order to calculate the on-line used fuzzy rules. Simulation results have showed that the proposed approach can mitigate the power source degradation and save the economic cost.

An Enhanced Deep Learning-Based Fusion Prognostic Method for RUL Prediction

This article proposes a novel deep learning based fusion prognostic method for remaining useful life (RUL) prediction of engineering systems. The proposed framework strategically combines the advantages of bidirectional long short-term memory (BLSTM) networks and particle filter (PF) method and meanwhile mitigates their limitations. In the proposed framework, BLSTM networks are applied for further extracting, selecting, and fusing discriminative features to form predicted measurements of the identified degradation indicator. Simultaneously, PF is utilized to estimate system state and identify unknown parameters of the degradation model for RUL prediction. Hence, the proposed fusion prognostic framework has two innovative features: first, the preprocessed features from raw multisensor data can be intelligently extracted, selected, and fused by the BLSTM networks without specific domain knowledge of feature engineering; second, the predicted measurements with uncertainties obtained from the BLSTM networks will be properly represented by the PF in a transparent manner. Moreover, the developed approach is experimentally validated with machining tool wear tests on a computer numerical control (CNC) milling machine. In addition, the popular techniques employed in this field are also investigated to compare with the proposed method.

A Novel Degradation Modeling and Prognostic Framework for Closed-Loop Systems With Degrading Actuator

This article presents a novel degradation modeling and prognostic method for a class of closed-loop feedback systems with degrading actuators. Toward this end, we first present a degradation modeling framework by integrating the stochastic degradation process model of the actuator and the state transition model of the system. This takes into consideration the mutual effects between the component-level degradation and system-level state. Then, the particle filter algorithm is utilized to jointly estimate the hidden degradation state of the actuator and the system state through indirect observations. Further, a time-varying nonlinear diffusion process equipped with two-stage parameters updating procedure is used to learn the evolving progression of the hidden degradation state. As such, a residual-threshold-based remaining useful life (RUL) prediction method is presented by simulating future system states and degradation trajectories based on the learned degradation process. As the application of the predicted RUL, a fault tolerant control method is presented by adjusting the controller parameter so as to extend the life of the system. Finally, a simulation study is conducted using a closed-loop control system in an inertial platform to verify the proposed method. The results indicate that the proposed method can reduce prognosis error, improve robustness, and extend the lifetime of the system.

Remaining useful life prediction for fractional degradation processes under varying modes

AbstractDegradation processes of practical chemical engineering systems are difficult to model accurately because of complicated nonlinearities, non‐stationarities, and non‐Markovian properties. Traditional prognostics techniques tend to neglect the multi‐mode switching issue, and therefore cannot be used for predicting the remaining useful life (RUL) of a piece of long‐life equipment, especially when it is continuously operated under varying modes. Specifically, the stationarity of differential data also plays an important role in depicting the evolutionary trend, and further affects the extrapolation procedure for RUL prediction. In this paper, we construct a multi‐mode degradation model with regard to fractional diffusion processes by considering both stationary and non‐stationary increments. The drift term is represented as a time‐related nonlinear function, while the diffusion term is driven by fractional Brownian motion (FBM) or sub‐fractional Brownian motion (sub‐FBM), according to the results of the stationary test. Based on the monitored data, the model is identified by combining change‐point detection and parameter estimation. The closed‐form distribution of RUL is then derived through a weak convergence transformation. A numerical example and a case study of a large blast furnace are provided to evaluate the performance of the proposed prognostics framework.

Design Prognostic Framework for Scene Classification in Video Processing

This paper provides the technique over selecting phenomenal frames extraction structure using SBD, KFE, and Semantic mechanism which quite easy to extract the valuable scene from the entire video. The proposed system will concentrates on findings the accuracy, precision and recall measure of inputted video from TRACEVID dataset. Here the proposed system signifies the use of thresholding technique and TBD of individual frame and calculate the various parameters like standard deviation, Mean etc. In order to legalize this claim, content based video reclamation systems were furnished using color histogram, features extraction and different approaches are applied for the supervision of the semantic temperament of each frame in the video. Also it gives major idea about the classification of the scene and algorithm which generates significant result.

An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data

In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring.

Prognosis of severe acquired brain injury: Short and long-term outcome determinants and their potential clinical relevance after rehabilitation. A comprehensive approach to analyze cohort studies.

BackgroundAccurate prognostic evaluation is a key factor in the clinical management of patients affected by severe acute brain injury (ABI) and helps planning focused therapies, better caregiver’s support and allocation of resources. Aim of the study was to assess factors independently associated with both the short and long-term outcomes after rehabilitation in patients affected by ABI in the setting of a single Rehabilitation Unit specifically allocated to these patients.Methods and findingsIn all patients (567) with age ≥ 18 years discharged from the Unit in the period 2006/2015 demographic, etiologic, comorbidity indicators, and descriptors of the disability burden (at hospital admission and discharge) were evaluated as potential prognostic factors of both short-term (4 classes of disability status at discharge) and long-term (mortality) outcomes. A comprehensive analytical method was adopted to combine several tasks. Select the factors with a significant independent association with the outcome, assess the relative weights and the “stability” (by bootstrap resampling) of them and estimate the role of the prognostic models in the clinical framework considering “cost” and “benefits”. The generalized ordered logistic model for ordinal dependent variables was used for the short-term outcome while the Cox proportional hazard model was used for the long-term outcome. The final short-term model identified 7 factors that independently account for 37% of the outcome variability as shown by pseudo R2 (pR2) = 0.37. The disability status descriptors show the strongest association since they account for more than 60% of the pR2, followed by age (14.8%), the presence of percutaneous endoscopic gastrostomy or nasogastric intubation (14.4%), a longer stay in the acute ward (5.9%) and concomitant coronary disease (1.3%). The final multivariable Cox model identified 4 factors that independently account for 52% of the outcome variability (R2 = 0.52). The disability extent and the disability recovered lead the long-term mortality since they account for the 53% of the global R2. The relevant effect of age (42%) is appreciable only after 2 years given the significant interaction with time. A longer stay in the acute ward explains the remaining fraction (5%). Considering ‘cost and benefits’, the decision curve analysis shows that the clinical benefit achieved by using both prognostic models is greater than the other possible action strategies, namely ‘treat all’ and ‘treat none. Several less obvious characteristics of the prognostic models are appreciated by integrating the results of multiple analytical methods.ConclusionThe comprehensive analytical tool aimed to integrate statistical significance, weight, “stability” and clinical “net” benefit, gives back a prognostic framework explaining a relevant portion of both outcomes’ variability in which the strong association of the disability status with both outcomes is comparable to and followed by a time modulated role of age. Our data do not support a differentiated association of traumatic vs non-traumatic etiology. The results encourage the use of integrated approach to analyze cohort data.

Prognostics Framework for Power Semiconductor IGBT Modules through Monitoring of the On-State Voltage

This paper presents a literature review and an overview of original contributions on diagnostic and prognostic technologies for IGBT power modules based on the monitoring of the on-state voltage (Von). 
 First, different kinds of Von sensing circuits are discussed in terms of specifications, implementation, performance and cost. A low-cost and practical circuit is experimentally demonstrated. Then, a method is presented and evaluated for estimating wire-bond degradation. Wire-bond lift-off is the most common wear-out mechanism for industrial power semiconductor IGBT modules. It is effectively detected by measuring Von at the Zero Temperature Coefficient (ZTC) current value. Next, a method is presented to estimate the die temperature. Knowing the die temperature allows estimating the thermo-mechanical stress imposed to the wire-bonds. It is demonstrated to be feasible with ±3°C accuracy/precision using after careful calibration of Von as a Temperature Sensitive Electrical Parameter (TSEP). Finally, an algorithm is presented to process the information generated from the monitoring of Von and estimate the Remaining Useful Life (RUL). It considers Von evolution prior the first wire-bond lift-off and it combines both condition-based and damage accumulation based predictions.
 The major contribution of this paper is to present, for the first time, a complete framework for diagnostics and prognostics of power semi-conductor IGBT modules.

A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor.

An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.

"How Long Have I Got?"-A Prospective Cohort Study Comparing Validated Prognostic Factors for Use in Patients with Advanced Cancer.

The optimal prognostic factors in patients with advanced cancer are not known, as a comparison of these is lacking. The aim of the present study was to determine the optimal prognostic factors by comparing validated factors. A multicenter, prospective observational cohort study recruited patients over 18 years with advanced cancer. The following were assessed: clinician-predicted survival (CPS), Eastern Cooperative Oncology Group performance status (ECOG-PS), patient reported outcome measures (anorexia, cognitive impairment, dyspnea, global health), metastatic disease, weight loss, modified Glasgow Prognostic Score (mGPS) based on C-reactive protein and albumin, lactate dehydrogenase (LDH), and white (WCC), neutrophil (NC), and lymphocyte cell counts. Survival at 1 and 3 months was assessed using area under the receiver operating curve and logistic regression analysis. Data were available on 478 patients, and the median survival was 4.27 (1.86-7.03) months. On univariate analysis, the following factors predicted death at 1 and 3 months: CPS, ECOG-PS, mGPS, WCC, NC (all p < .001), dyspnea, global health (both p ≤ .001), cognitive impairment, anorexia, LDH (all p < .01), and weight loss (p < .05). On multivariate analysis ECOG-PS, mGPS, and NC were independent predictors of survival at 1 and 3 months (all p < .01). The simple combination of ECOG-PS and mGPS is an important novel prognostic framework which can alert clinicians to patients with good performance status who are at increased risk of having a higher symptom burden and dying at 3 months. From the recent literature it is likely that this framework will also be useful in referral for early palliative care with 6-24 months survival. This large cohort study examined all validated prognostic factors in a head-to-head comparison and demonstrated the superior prognostic value of the Eastern Cooperative Oncology Group performance status (ECOG-PS)/modified Glasgow Prognostic Score (mGPS) combination over other prognostic factors. This combination is simple, accurate, and also relates to quality of life. It may be useful in identifying patients who may benefit from early referral to palliative care. It is proposed ECOG-PS/mGPS as the new prognostic domain in patients with advanced cancer.

Proving non-conventional methods: A paradigmatic paradox

Originally, it was thought that Evidence-Based Medicine (EBM) would supplant decision-making based on intuition or plausibility. Later, it appeared that the gold standard in EBM, the Randomised Controlled Trial (RCT), was not as 'hard' a reference point as had been supposed, and an assessment of credibility was needed. After some decades, 'credible' RCT-centred EBM has led to the dismissal of therapies deemed to be implausible, such as Homoeopathy. Nevertheless, such therapies remain widely appreciated by patients who use them alongside conventional medicine. Nearly two hundred RCTs of Homoeopathy showed no difference in efficacy between Homoeopathy and comparable conventional trials. There is no proof that conventional trials are of better quality and there is no proof of harm by Homoeopathy. However, selective analysis of evidence shows statistically insignificant results, then interpreted as unscientific 'confirmation' of the hypothesis that Homoeopathy is a placebo. As a result, the use of Homoeopathy instead of antibiotics in acute respiratory tract infections has been discouraged, despite the absence of evidence of the efficacy of antibiotics for this indication and an established risk of harm. Complex statistical interpretations of RCT evidence lead to impractical and even harmful advice. Credible proof is actually based on many subjective (often continuous) variables. As an endpoint for EBM, Bayesian probabilities, based on more than RCT evidence, would provide a more practical and personalised type of advice for patients, and would develop the diagnostic process into a prognostic framework, offering alternatives if one particular solution was to fail.

Predicting Damage and Life Expectancy of Subsea Power Cables in Offshore Renewable Energy Applications

Subsea power cables are critical assets within the distribution and transmission infrastructure of electrical networks. Over the past two decades, the size of investments in subsea power cable installation projects has been growing significantly. However, the analysis of historical failure data shows that the present state-of-the-art monitoring technologies do not detect about 70% of the failure modes in subsea power cables. This paper presents a modeling methodology for predicting damage along the length of subsea cables due to environmental conditions (e.g., seabed roughness and tidal flows) which result in the loss of the protective layers on the cable due to corrosion and abrasion (accounting for over 40% of subsea cable failures). For a defined cable layout on different seabed conditions and tidal current inputs, the model calculates the cable movement by taking into account the scouring effect and then it predicts the rate at which the material is lost due to corrosion and abrasion. Our approach integrates accelerated aging data using a Taber test which provides abrasion wear coefficients for the cable materials. The models have been embedded into a software tool that predicts the life expectancy of the cable and demonstrated for narrow conditions, where the tidal flow is unidirectional and perpendicular to the power cable. The paper also provides discussion on how the developed models can be used with other condition monitoring data sets in a prognostics framework.

A neural network framework for similarity-based prognostics

Prognostic performance is associated with accurately estimating remaining useful life. Difficulty in accurate prognostic applications can be tackled by processing raw sensor readings into more meaningful and comprehensive health condition indicators that will then provide performance information for remaining useful life estimations. To that end, typically, multiple tasks on data pre-processing and predictions have to be carried out such that tasks can be assessed using different methodological aspects. However, incompatible methods may result in poor performance and consequently lead to undesirable error rates.The present research evaluates data training and prediction stages. A data-driven prognostic method based on a feed-forward neural network framework is first defined to calculate the performance of a complex system. Then, the health indicators are used in a similarity based remaining useful life estimation method. This framework presents a conceptual prognostic protocol that overcomes challenges presented by multi-regime condition monitoring data.

A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW

A steam turbine is one of the critical components in a power generation system whose failure may result in unexpected consequences, even catastrophic losses. Thus, the reliability of steam turbines needs to be guaranteed all the time, which requires that its health state can be monitored and predicted effectively. Due to various failure modes, it is difficult to build physics-of-failure models used for health prognostics for steam turbines. In this paper, a data-driven integrated framework for health prognostics for steam turbines, which is based on extreme gradient boosting (XGBoost) and dynamic time warping (DTW), is proposed. The proposed framework includes two modules: anomaly detection and remaining useful life (RUL) prediction. The anomalies refer to the overall abnormal operation of steam turbines. In the process of anomaly detection, the temporal variables which can represent the operating conditions of the considered steam turbine are selected first. Appropriately selected temporal variables can reduce the input dimension and will improve real-time performance. Then, XGBoost is used to detect anomalies based on learning historical data. In the process of RUL prediction, a similarity-based algorithm with DTW is used to gain the RUL by contrasting the measured temporal variables with those in the historical cases. The similarity-based algorithm can predict the RUL without establishing a degradation path model, which can avoid the difficulties in parameter estimation for the degradation model and model generalization. The proposed framework is validated by real case studies from an industrial steam turbine. The results show that the proposed approach can detect the anomalies successfully and predict the RUL effectively.

A Robust Hybrid Filtering Method for Accurate Battery Remaining Useful Life Prediction

Accurate remaining useful life (RUL) prediction under the noisy environment is a big challenge for the health management of modern industrial systems since the extraction of the accurate data structure from heavily corrupted data is difficult. In recent years, the kernel adaptive filter (KAF) has been widely adopted to solve the robust regression problem due to its low-complexity and high-approximation capability and robustness while the applications in battery RUL prediction are still few and far between. Thus, this paper is concerned with long-term RUL prediction using the KAF method. At first, concretely speaking, a robust KAF algorithm is derived based on the double-Gaussian-mixture (DGM) cost function, which is used to learn the capacity degradation mechanism from contaminated capacity data and so as to build the long-term prediction model. Second, a robust unscented Kalman filter (UKF) algorithm employing the DGM-based cost function is developed, which is then combined with the KAF-based prediction model to realize a more accurate and reliable prediction. Under the hybrid prognostic framework, the proposed UKF algorithm is applied to filter the noisy observations. When the observation data are inaccessible, the predicted data from the off-line trained KAF-based prediction model are adopted as the approximated value of the real observations for the UKF algorithm to optimize the prediction results and to provide the uncertainty representation. The experimental results reveal that the proposed method has great robustness when the measurements contain noise and large outliers, which makes it possible to get satisfactory prediction performance without preprocessing the data manually.

Ensemble of Models for Fatigue Crack Growth Prognostics

Various models of fatigue crack growth in different scenarios have been proposed in the literature. Here, in this paper, we propose a general prognostic framework for tracking crack evolution in equipment undergoing fatigue and predicting the Remaining Useful Life (RUL). The main contribution of this work is to integrate Particle Filtering (PF) and a new ensemble model which combines diverse physical degradation models with respect to their accuracy performance in previous time steps, in order to maximize the overall prediction capability. To validate the effectiveness of the proposed framework, a case study concerning multiple fatigue crack growth degradations is extensively investigated.