System Remaining Useful Life Research Articles

Probabilistic Deep Learning With Bayesian Networks for Predicting Complex Engineering Systems' Remaining Useful Life: A Case Study of Unmanned Surface Vessel

Abstract Remaining useful life (RUL) serves as a key indicator of system health, and its accurate and timely prediction supports informed decision-making for efficient operation and maintenance. This is essential for complex engineering systems (CESes) such as unmanned surface vessels (USVs), where the human operators have limited opportunity to intervene during the operation. This paper proposes a framework for predicting the RUL of the CESes. The proposed framework employs a probabilistic deep learning (PDL) approach to predict the component's RUL and an equation node-based Bayesian network (BN) to predict system RUL (SRUL) at any future time-step. The component-level RUL method is validated using the NASA's Commercial Modular Aero-Propulsion System Simulation (c-mapss) dataset, and then the proposed framework is demonstrated with a USV case study. The results are evaluated using a set of quality metrics. By making use of the condition-monitoring sensor data, component reliability data, and models that account for the complex causal relationships between components, the proposed framework can provide near real-time predictions of the RUL with uncertainty of a CES, thus supporting its informed decision-making during the operation.

Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Multi-objective predictive maintenance scheduling models integrating remaining useful life prediction and maintenance decisions

Multi-objective predictive maintenance scheduling models integrating remaining useful life prediction and maintenance decisions

Advanced Prognostic Models for Bearing Health: A Comparative Analysis of BiLSTM and ANFIS

Bearings play a critical role in the operation of rotary machines, serving as essential components. Their failure often leads to unexpected shutdowns, posing a significant risk to the entire system. To mitigate these risks, it is imperative to implement proactive maintenance measures and strategic planning to prevent system breakdowns. This article introduces a comparative analysis between two predictive modelling approaches: Bidirectional Long Short-Term Memory (Bi-LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) networks, aiming to enhance bearing prognostics. The proposed methodology involves a two-step process. Firstly, data undergoes pre-processing through wavelet packet decomposition (WPD). Subsequently, a degradation model is employed for predicting the remaining useful life (RUL). To validate the accuracy of the proposed approach, extensive testing is conducted using a bearing's life dataset obtained from a run-to-failure experiment. The results demonstrate that the ANFIS model exhibits remarkable capabilities in learning and accurately estimating the system's RUL, achieving this with minimal computation time compared to alternative methods, thus presenting a more efficient and precise solution.

Novel Prognostic Methodology of Bootstrap Forest and Hyperbolic Tangent Boosted Neural Network for Aircraft System

Complex aviation systems’ integrity deteriorates over time due to operational factors; hence, the ability to forecast component remaining useful life (RUL) is vital to their optimal operation. Data-driven prognostic models are essential for system RUL prediction. These models benefit run-to-failure datasets the most. Thus, significant factors that could affect systematic integrity must be examined to quantify the operational component of RUL. To expand predictive approaches, the authors of this research developed a novel method for calculating the RUL of a group of aircraft engines using the N-CMAPSS dataset, which provides simulated degradation trajectories under real flight conditions. They offered bootstrap trees and hyperbolic tangent NtanH(3)Boost(20) neural networks as prognostic alternatives. The hyperbolic tangent boosted neural network uses damage propagation modelling based on earlier research and adds two accuracy levels. The suggested neural network architecture activates with the hyperbolic tangent function. This extension links the deterioration process to its operating history, improving degradation modelling. During validation, models accurately predicted observed flight cycles with 95–97% accuracy. We can use this work to combine prognostic approaches to extend the lifespan of critical aircraft systems and assist maintenance approaches in reducing operational and environmental hazards, all while maintaining normal operation. The proposed methodology yields promising results, making it suitable for adoption due to its relevance to prognostic difficulties.

Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method

Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method

Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault

Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault

Remaining useful life prediction based on a PCA and similarity methods

Aircraft engine failures or damages not only incur substantial financial losses but also present risks of injuries or even fatalities. Hence, it is of utmost importance to devise an effective method to predict potential failures in advance, thereby mitigating accidents and minimizing losses. This paper proposes a novel approach that combines a principal component analysis (PCA) with similarity methods to establish a degradation trajectory database and predict the remaining useful life (RUL) of new engines by identifying similar trajectories. Firstly, the data dimensionality is reduced using a PCA to create a health indicator. The validity of the reduced data is confirmed by calculating the Spearman correlation coefficient between the health indicator and the system RUL. During the similarity comparison process, the Manhattan distance is employed for the similarity calculation, and parameter optimization is performed on the length of selected time segments and the number of chosen similar trajectories to optimize the similarity of RUL prediction model, resulting in the optimal prediction results among all engine test sets. Notably, this paper introduces the feasibility of employing the Manhattan distance in similarity method-based prediction, which is superior to the commonly used Euclidean distance calculation method found in most literature. This finding offers innovative ideas and perspectives for advancing RUL prediction methodologies. By adopting the proposed approach, the occurrence of accidents and losses associated with aircraft engine failures can be substantially reduced, leading to enhanced safety and economic benefits.

Integrated system-level prognosis for hybrid systems subjected to multiple intermittent faults

Integrated system-level prognosis for hybrid systems subjected to multiple intermittent faults

A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system

A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system

Remaining Useful Life Estimation for Underground Cable Systems Based on Historical Health Index

In this paper, a modeling method for estimating the remaining useful life (RUL) of aged underground cable systems is proposed that uses statistical health index (HI) and operating factor (OF) data of retired systems. The HI is an indicator which identifies the condition of an underground cable system and its components and is calculated from testing and inspection results. The OF takes actual operating conditions and technical data from the system into consideration. Both factors are then combined to determine the overall health index (OHI) of each system. For RUL estimation of underground cable systems, normal distribution and Weibull distribution analyses are first applied to determine a health index curve and an aging line. The relationship between these two curves gives an estimate of the system’s apparent age. The RUL of the system is then calculated in terms of the difference between its apparent age and its actual chronological age. In this study, thirteen retired systems and ten operating systems were evaluated and analyzed, and accurate results were obtained. Using the methods described here, the apparent age and the RUL of underground cable systems can be accurately estimated. Finally, a maintenance strategy for underground cable systems is recommended, which promises more efficient maintenance and greater cost-effectiveness in the management of underground cable systems.

RUL management by production reference loopback

Online remaining useful life (RUL) assessment is a significant asset in prognostic and health management (PHM) in many industrial domains where safety, reliability, and cost reduction are of high importance. It is not easy to predict the breakdown state of a system when it operates under multiple operating conditions, because system degradation varies with the dynamics of the operations. This paper presents an Input-Output Hidden Markov Model (IOHMM) that estimates the RUL in real time based on available measurements. The model learns the impact of the operating condition on the RUL and allows to manage the system RUL by changing the corresponding operating conditions. A reference managing algorithm is presented to match the estimated RUL to a given target RUL. In addition, well-known algorithms are adapted from HMM to IOHMM and are used for model training and health state diagnostics. A numerical application is proposed to show the importance of obtaining good predictions from a limited amount of data sequences. Specifically, since degradation is a slow process, it is difficult to have a large amount of data sequences in order to predict the RUL more accurately until the failure. Therefore, the bootstrap method with data resampling and replacement is used to train the IOHMM model to improve estimation accuracy.

Remaining useful life estimation of turbofan engines using adaptive fault detection learning

Failure prognostics have greatly enhanced the predictive maintenance of industrial systems by providing the remaining useful life (RUL) information, offering opportunities for high reliability, availability, maintainability and safety. To do so, historical monitoring data are injected into machine learning model to learn how to predict the RUL and then, in an online phase directly estimate the RUL of a new similar system. However, in case of multiple degradation trends representing multiple systems, it lead to different times of anomaly appearance and therefore various RUL values for learning. This situation makes difficult to train the predictor and use in this case an approximated unique RUL value. Hence, this paper proposes an adaptive anomaly detection methodology to identify the times of fault occurrence, and then assign the correct RUL values of each failure trajectory to the train the predictor. This methodology will facilitate the learning task for an accurate prediction of system RUL. The performance of the proposed methodology is highlighted using a long short-term memory (LSTM) network with the accelerated run to failure data of turbofan engines provided by the NASA to estimate the RUL.

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process anddown stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Dealing with prognostics uncertainties: Combination of direct and recursive remaining useful life estimations

Dealing with prognostics uncertainties: Combination of direct and recursive remaining useful life estimations

System-level failure prognostics: Literature review and main challenges

This paper reviews methods and practices for addressing the concepts of system-level prognostics (SLP) and system remaining useful life (SRUL) estimation applied to multicomponent systems. A precise definition of SLP is provided, emphasizing the advantages of its use in terms of identifying the scope of SLP applications. In addition, a comprehensive review of the literature is provided to properly classify and compare the findings of previously published studies in the field of SLP and evaluate the effectiveness of the available methodologies within the different stages of prognostic development. Finally, and considering that SLP is still a relatively recent research field, we also provide a thorough discussion on the main challenges that remain to be solved before achieving complete technology transfer, as well as future research directions.

Remaining useful life prediction in prognostics using multi-scale sequence and Long Short-Term Memory network⋆

Remaining useful life prediction in prognostics using multi-scale sequence and Long Short-Term Memory network⋆

Methodology on Establishing Multivariate Failure Thresholds for Improved Remaining Useful Life Prediction in PHM

Prognostics and Health Management (PHM) methodologies and techniques have been much widely studied in the academia and practiced by the industry in recent years. Prognostic approaches commonly try to establish the relationship between Remaining Useful Life (RUL) and a single variable or health indicator (HI) which can be obtained from multi-sensor fusion or data-driven models. However, simply relying on a single variable could reduce RUL prediction robustness when it is less representative of the system health conditions. Taking multiple variables into consideration for RUL prediction, quantifying operating risks and determining multivariate failure threshold is essential yet rarely studied. Generally, there are three major challenges that limit the practicality of this topic. 1) How to determine the multivariate failure threshold? 2) How to quantify operation risks based on multiple variables? 3) How to make reliable extrapolations of future conditions? To address these questions, this paper proposes 1) a novel copula model to determine multivariate failure threshold, and 2) a Maximum Likelihood Estimation enhanced similarity-based Particle Filter (MLE-SMPF) to predict future system conditions. In the proposed methodology, the health assessment is firstly performed to obtain HI trajectory. The copula risk quantification model is then trained by two variables HI and life. The proposed copula model can easily include multiple variables compared with our previously published approach using bivariate Weibull Distribution[1]. Afterward, MLE-SMPF is used to extrapolate future HI for testing data. The prediction capability is further improved compared with [2] by introducing MLE for Particle Filter transition function parameter initialization. Finally, the system RUL is determined from the failure threshold which is obtained according to the quantified operation risk. The proposed methodology is validated on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The result outperforms most of the benchmarks from recent publications. The proposed methodology is easy to transfer to other potential machine prognostic applications.

Fresh new look for system-level prognostics

Model-based prognostic approaches use first-principle or regression models to estimate and predict the system’s health state in order to determine the remaining useful life (RUL). Then, in order to handle the prediction results uncertainty, the Bayesian framework is usually used, in which the prior estimates are updated by infield measurements without changing the model parameters. Nevertheless, in the case of system-level prognostic, the mere updating of the prior estimates, based on a predetermined model, is no longer sufficient. This is due to the mutual interactions between components that increase the system modeling uncertainties and may lead to an inaccurate prediction of the system RUL (SRUL). Therefore, this paper proposes a new methodology for online joint uncertainty quantification and model estimation based on particle filtering (PF) and gradient descent (GD). In detail, the inoperability input-output model (IIM) is used to characterize system degradations considering interactions between components and effects of the mission profile; and then the inoperability of system components is estimated in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, GD is used to correct and to adapt the IIM parameters. To illustrate the effectiveness of the proposed methodology and its suitability for an online implementation, the Tennessee Eastman Process is investigated as a case study.