Commercial Modular Aero-Propulsion System Simulation Research Articles

A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction.

Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.

Hhsmm: an R package for hidden hybrid Markov/semi-Markov models

This paper introduces the hhsmm R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. These models are flexible models with both Markovian and semi-Markovian states, which are applied to situations where the model involves absorbing or macro-states. The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction of future states and the residual useful lifetime estimation in the predict function. The commercial modular aero-propulsion system simulation (C-MAPSS) data-set is also included in the package, which is used for illustration of the application of the package features. The application of the hhsmm package to the analysis and prediction of the Spain’s energy demand is also presented.

A Prognostic and Health Management Framework for Aero-Engines Based on a Dynamic Probability Model and LSTM Network

In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive density peaks clustering algorithm, which has the advantages of an extremely short training time and high enough precision, is employed for modelling engine fault development from the beginning of engine service, and principal component analysis is introduced to convert complex high-dimensional raw data into low-dimensional data. The model can be updated from time to time according to the accumulation of engine data to capture the occurrence and evolution process of engine faults. In order to address the problems with the commonly used data driven methods, the DP + LSTM model is employed to estimate the remaining useful life (RUL) of the engine. Finally, the proposed PHM framework is validated experimentally using NASA’s commercial modular aero-propulsion system simulation dataset, and the results indicate that the DP model has higher stability than the classical artificial neural network method in fault diagnosis, whereas the DP + LSTM model has higher accuracy in RUL estimation than other classical deep learning methods.

Distance self-attention network method for remaining useful life estimation of aeroengine with parallel computing

Remaining useful life (RUL) estimation of aeroengine is significant in the health monitoring, operation and maintenance of aircrafts. Traditional deep learning methods fail to consider the degradation rules of aeroengine and have low computational efficiency for RUL estimation. Therefore, a novel deep learning architecture called distance self-attention network (DSAN) is developed based on self-attention and parallel computing on time series. In the proposed DSAN method, a distance function is developed to improve the matching ability of self-attentions and optimize the feature extraction capability, and the fusion layer inspired by the computation of recurrent neural network (RNN) is developed to fuse historical information and real-time data. The effectiveness of the DSAN method for RUL estimation is validated by utilizing the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) data provided by NASA. It is revealed that the DSAN method is superior to the typical methods such as convolutional neural network (CNN) and long-short term memory (LSTM), because the root mean square error (RMSE) decreased by 7.3%∼ 25.3%, and the Score reduced by 28% ∼51.8%. The efforts of this paper provide a promising method for aeroengine RUL estimation, which has the potential to support the health monitoring and predictive maintenance of multi-sensor systems.

Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion

In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to effectively describe degradation trends, and poor remaining useful life (RUL) prognosis effects, a remaining useful life prognosis model combining an improved stack sparse autoencoder (imSSAE) and an improved echo state network (imESN) is proposed in this paper. First, the 3-sigma criterion is adopted to remove the noise and reconstruct the data, and then the deep features of the engine are extracted by using an imSSAE and fused into health indicator (HI) curves describing the engine degradation trend. Finally, an attention mechanism is introduced into an imESN to adaptively process different types of data and obtain the RUL. The experimental results based on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset show that compared with the other popular RUL prediction models, the combined model proposed in this paper has higher prediction accuracy, and the evaluation indices also show the effectiveness and superiority of the model.

Prediction of Remaining Useful Life Using Fused Deep Learning Models: A Case Study of Turbofan Engines

Abstract The study of intelligent operation and maintenance methods for turbofan engines is of great importance for improving the reliability of turbofan engines. Given the harsh operating conditions and complex structure of the turbofan engine, it is extremely difficult to establish an accurate physical model for remaining useful life (RUL) prediction. The traditional operation and maintenance method based on the physical model has several limitations in the application of turbofan engines, while the data-driven method offers a new solution. Compared with traditional machine learning models, deep learning models possess more powerful nonlinear expression capabilities and feature extraction capabilities. Therefore, this study focuses on studying the RUL prediction algorithm for turbofan engines based on the fused deep learning models. In this article, a multimodal deep learning approach based on a 1DCNN (1D convolutional neural network) + attention enhanced Bi-LSTM (bidirectional long short-term memory) network is proposed to predict the RUL by mining the temporal information of data. Furthermore, a DDResNet (dilated deep residual network) is also introduced to the 1DCNN submodel to leverage its hidden pattern mining capability due to its advance in preventing performance degradation across layers. Subsequently, the output of these two submodels is weighted fused to obtain the final RUL prediction. The merits of the proposed method are demonstrated by comparing it with existing methods for RUL prediction using the C-MAPSS (commercial modular aero-propulsion system simulation) dataset.

Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network

The Machine Prognostics and Health Management (PHM) are concerned with the prediction of the Remaining Useful Lifetime (RUL) of assets. Accurate real-time RUL predictions are necessary when developing an efficient predictive maintenance (PdM) framework for equipment health assessment. If correctly implemented, a PdM framework can maximize the interval between maintenance operations, minimize the cost and number of unscheduled maintenance operations, and improve overall availability of the large facilities like nuclear power plants (NPPs). This is especially important for nuclear power facilities to maximize capacity factor and reliability. In this work, we propose a data-driven approach to make predictions of both the RUL and its uncertainty using a Bayesian Neural Network (BNN). The BNN utilizes the Bayes by backprop algorithm with variational inference to estimate the posterior distribution for each trainable parameter so that the model output is also a PDF from which one can draw the mean prediction and the associated uncertainty. To learn the correlations between various time-series sensor data measurements, a time window approach is implemented with a two-stage noise filtering process for incoming sensor measurements to enhance the feature extraction and overall model performance. As a proof of concept, the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPPS) datasets are utilized to assess the performance of the BNN model. The modeled system can be treated as a surrogate for turbine generators used in NPPs due to the similar mode of operation, degradation, and measurable variables. Comparisons against other state-of-the-art algorithms on the same datasets indicate that the BNN model can not only make predictions with comparable level of accuracy, but also offer the benefit of estimating uncertainty associated with the prediction. This additional uncertainty, which can be continuously updated as more measurement data are collected, can facilitate the decision-making process with a quantifiable confidence level within a PdM framework. Additional advantages of the BNN are showcased, such as providing component maintenance ranges and model executing frequency, with an example of how the BNN estimated uncertainty can be used to support the continuous predictive maintenance. A PdM framework based on a BNN will allow for utilities to make more informed decisions on the optimal time for maintenance so that the loss of revenue can be minimized from planned and unplanned maintenance outages.

Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision

RUL estimation plays a vital role in effectively scheduling maintenance operations. Unfortunately, it suffers from a severe data imbalance where data from machines near their end of life is rare. Additionally, the data produced by a machine can only be labeled after the machine failed. Both of these points make using data-driven methods for RUL estimation difficult. Semi-Supervised Learning (SSL) can incorporate the unlabeled data produced by machines that did not yet fail into data-driven methods. Previous work on SSL evaluated approaches under unrealistic conditions where the data near failure was still available. Even so, only moderate improvements were made. This paper defines more realistic evaluation conditions and proposes a novel SSL approach based on self-supervised pre-training. The method can outperform two competing approaches from the literature and the supervised baseline on the NASA Commercial Modular Aero-Propulsion System Simulation dataset.

A Novel Predictive Selective Maintenance Strategy Using Deep Learning and Mathematical Programming

Many systems are required to perform a series of missions with finite breaks between successive missions. For such systems, one of the most widely used maintenance strategies is selective maintenance (SM). Under certain maintenance constraints, the SM problem (SMP) consist in selecting an optimal subset of feasible maintenance actions to maximize the system reliability for the upcoming mission. Almost all SMP models proposed in the literature are focused on traditional physics-based reliability models, where component lifetimes can be modeled using a stochastic process. With the application of new technologies such as wireless sensors and Industrial Internet of Things (IIoT), and the recent advancements in Deep Learning (DL) algorithms for prognostics, predictive maintenance based on data-driven methods has become a very popular maintenance strategy. These data driven methods have shown extreme accuracy in predicting remaining useful life (RUL) of components and systems. The goal of this paper is to introduce a predictive selective maintenance strategy that can be used to solve complex and relatively large multi-component systems. A DL algorithm will be used to estimate the probability that each component will successfully complete the upcoming mission, a selective maintenance optimization model will then be used to identify the maintenance actions that will maximize the system reliability. An efficient solution method is devised to solve the resulting complex optimization problem. The NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is used to train and evaluate the DL algorithm. The numerical experiments carried out show that the proposed novel predictive maintenance strategy is accurate and yields valid decisions.

Aero-Engine Remaining Useful Life Estimation Based on Multi-Head Networks

Data-driven aero-engine remaining useful life (RUL) estimation is a key technology to monitor engine’s degradation. However, due to the difficulties of extracting the time information from data, the accuracy of data-driven methods remains low. Aiming at the problem, this article proposes a novel multi-head structure to wrap time information into the network structure and training processes. Multi-head networks are designed to take the time sequence information of inputs in the feedforward process. Meanwhile, the time loss function takes full consideration of the time prior information of the outputs (estimated RUL) and directs the backward process of the networks to converge to the real aero-engine operating situation. Experiments on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) aero-engine’s RUL estimation dataset are conducted to validate the effectiveness of the proposed method. The result and comparisons with other state-of-the-art methods show that the proposed multi-head structure and time loss function can improve the accuracy significantly. This suggests that the proposed method is a promising approach in aero-engine degradation evaluation.

Bi-LSTM-Based Two-Stream Network for Machine Remaining Useful Life Prediction

In industry, prognostics and health management (PHM) is used to improve the system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failure and reducing operation cost. Recently, with the development of deep learning technology, long short-term memory (LSTM) and convolutional neural networks (CNNs) are adopted into many RUL prediction approaches, which shows impressive performances. However, existing deep learning-based methods directly utilize raw signals. Since noise widely exists in raw signals, the quality of these approaches’ feature representation is degraded, which degenerates their RUL prediction accuracy. To address this issue, we first propose a series of new handcrafted feature flows (HFFs), which can suppress the raw signal noise and thus improve the encoded sequential information for the RUL prediction. In addition, to effectively integrate our proposed HFFs with the raw input signals, a novel bidirectional LSTM (Bi-LSTM)-based two-stream network is proposed. In this novel two-stream network, three different fusion methods are designed to investigate how to combine both streams’ feature representations in a reasonable way. To verify our proposed Bi-LSTM-based two-stream network, extensive experiments are carried out on the commercial modular aero propulsion system simulation (C-MAPSS) dataset, showing superior performances over state-of-the-art approaches.

A Prediction Method for the RUL of Equipment for Missing Data

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available and physical modeling is often not feasible. In this paper, a deep convolutional neural network (CNN) architecture is investigated for its ability to estimate the RUL of turbofan engines. The input to the model is a window of time series data collected from the engine under test. Inputting raw sensor data allows features to be learned instead of manually determined. To incorporate the ability to detect features of differing lengths, inception modules are used in the neural network architecture. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set and high prognosis accuracy was achieved. The developed model was used in the 2021 PHM Society Data Challenge and received second place, further validating its ability to accurately estimate RUL.

PERFORMANCE EVALUATION OF DATA-DRIVEN PROGNOSTIC BASED ON RVM-SBI TECHNIQUE

The Prognostic and Health Management (PHM) becomes a research topic in its own right and tends to be more and more visible within the scientific community such as in Nasa Society, which has provided datasets for experiments. The purpose of this paper is to evaluate the performance of a data-driven prognostic technique used for predicting Remaining Useful Life (RUL). The methodological support of the proposed approach integrates all data-driven prognostic sequential steps merged in offline and online part. To design the predictive degradation model on the offline part, the Relevance Vector Machine (RVM) algorithm was applied. On the online part, prediction of the RUL is based on the Similarity-Based Interpolation (SBI) algorithm. The different steps of the methodology are described and their implementation undertaken through a case study involving the degradation dataset of turbofan engines from the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). Finally, results are compared with other techniques applied on the same dataset.

Remaining Useful Life Estimation Using Neural Ordinary Differential Equations

Data-driven machinery prognostics has seen increasing popularity recently, especially with the effectiveness of deep learning methods growing. However, deep learning methods lack useful properties such as the lack of uncertainty quantification of their outputs and have a black-box nature. Neural ordinary differential equations (NODEs) use neural networks to define differential equations that propagate data from the inputs to the outputs. They can be seen as a continuous generalization of a popular network architecture used for image recognition known as the Residual Network (ResNet). This paper compares the performance of each network for machinery prognostics tasks to show the validity of Neural ODEs in machinery prognostics. The comparison is done using NASA’s Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, which simulates the sensor information of degrading turbofan engines. To compare both architectures, they are set up as convolutional neural networks and the sensors are transformed to the time-frequency domain through the short-time Fourier transform (STFT). The spectrograms from the STFT are the input images to the networks and the output is the estimated RUL; hence, the task is turned into an image recognition task. The results found NODEs can compete with state-of-the-art machinery prognostics methods. While it does not beat the state-of-the-art method, it is close enough that it could warrant further research into using NODEs. The potential benefits of using NODEs instead of other network architectures are also discussed in this work.

A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

Deep learning-based approach has emerged as a promising solution to handle big machinery data from multi-sensor suites in complex physical assets and predict their remaining useful life (RUL). However, most recent deep learning-based approaches deliver a single-point estimate of RUL as these models represent the weights of a neural network as a deterministic value and hence cannot convey uncertainty in the RUL prediction. This practice usually provides overly confident predictions that might cause severe consequences in safety-critical industries. To address this issue, this paper proposes a probabilistic Bayesian recurrent neural network (RNN) for RUL prognostics considering epistemic and aleatory uncertainties. The epistemic uncertainty is handled by Bayesian RNN layers as extensions from the Frequentist RNN layers using the Flipout method. The aleatory uncertainty is covered by a probabilistic output that follows a Gaussian distribution parameterized by the two neurons in the output layer. The network is trained using Bayes by backprop with the Flipout method. The proposed model is demonstrated by the open-access Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of turbofan engines and a comparative study of the Frequentist RNN counterparts, the Monte Carlo Dropout-based RNN, and the state-of-the-art models for C-MAPSS datasets. The results demonstrate the promising performance and robustness of the proposed model in RUL prognostics.

Evolutionary neural architecture search for remaining useful life prediction

With the advent of Industry 4.0, making accurate predictions of the remaining useful life (RUL) of industrial components has become a crucial aspect in predictive maintenance (PdM). To this aim, various Deep Neural Network (DNN) models have been proposed in the recent literature. However, while the architectures of these models have a large impact on their performance, they are usually determined empirically. To exclude the time-consuming process and the unnecessary computational cost of manually engineering these models, we present a Neural Architecture Search (NAS) technique based on an Evolutionary Algorithm (EA) applied to optimize the architecture of a DNN used to predict the RUL. The EA explores the combinatorial parameter space of a multi-head Convolutional Neural Network with Long Short Term Memory (CNN-LSTM) to search for the best architecture. In particular, our method requires minimum computational resources by making use of an early stopping policy and a history of the evaluated architectures. We dub the proposed method ENAS-PdM. To our knowledge, this is the first work where an EA-based NAS is used to optimize a CNN-LSTM architecture in the field of PdM. In our experiments, we use the well-established Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from NASA. Compared to the current state-of-the-art, our method obtains better results in terms of two different metrics, RMSE and Score, when aggregating across all the C-MAPSS sub-datasets. Without aggregation, we achieve lower RMSE in 3 out of 4 sub-datasets. Our experimental results verify that the proposed method is a reliable tool for obtaining state-of-the-art RUL predictions and as such it can have a strong impact in several industrial applications, especially those with limited available computing power.

Performance degradation prediction of aeroengine based on attention model and support vector regression

Accurate performance degradation prediction of aeroengines can ensure the safety and reliability of the aircraft. Based on the mass long time series data of multiple state parameters, a novel performance degradation prediction method based on attention model (AM) and support vector regression (SVR) is proposed in this article. The AM uses the attention mechanism between encoder and decoder to realize weight distribution of different source samples, so as to realize time series prediction of state parameters. The SVR model is used to mine the mapping relationship between multiple state parameters and performance degradation. The performance degradation prediction results can be achieved by putting the time series prediction results of multiple state parameters into the SVR model. The turbofan engine degradation simulation dataset carried out using commercial modular aero-propulsion system simulation (C-MAPSS) is used to verify the effectiveness of the proposed method. The results demonstrate that it can get accurate time series prediction and performance degradation analysis results. Compared with other methods, the proposed attention model and support vector regression (AM-SVR) model has lower prediction error and higher stability when dealing with noised samples.

Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension

Many machine learning algorithms and models have been proposed in the literature for predicting the remaining useful life (RUL) of systems and components that are subject to condition monitoring (CM). However, in cases where data is ubiquitous, identifying the most suitable equipment for life-extension based on CM data and RUL predictions is a rather challenging task. This paper proposes a technique for determining and prioritizing high-value assets for life-extension treatments when they reach the end of their useful life. The technique exploits the use of key concepts in machine learning (such as data mining and k-means clustering) in combination with an important tool from reliability-centered maintenance (RCM) called the potential-failure (P-F) curve. The RCM process identifies essential equipment within a plant which are worth monitoring, and then derives the P-F curves for equipment using CM and operational data. Afterwards, a new index called the potential failure interval factor (PFIF) is calculated for each equipment or unit, serving as a health indicator. Subsequently, the units are grouped in two ways: (i) a regression model in combination with suitably defined PFIF window boundaries, (ii) a k-means clustering algorithm based on equipment with similar data features. The most suitable equipment for life-extension are identified in groups in order to aid in planning, decision-making and deployment of maintenance resources. Finally, the technique is empirically tested on NASA’s Commercial Modular Aero-Propulsion System Simulation datasets and the results are discussed in detail.

Real-Time Prognostics of Engineered Systems under Time Varying External Conditions Based on the COX PHM and VARX Hybrid Approach.

In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the challenge mentioned above, a hybrid method for predicting the reliability index and the Remaining Useful Life (RUL) of engineered systems under time-varying external conditions is proposed in this paper. The proposed method is competent in reflecting the influence of time-varying external conditions on the degradation behaviour of engineered systems. Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. During the reliability prediction, the Fourier Grey Model (FGM) is employed with the time series models for long-term forecasting of the external conditions. The results show that the method that is proposed in this paper is competent for the RUL prediction as compared with baseline approaches.