Commercial Modular Aero-Propulsion System Simulation Research Articles

A machine learning approach to circumventing the curse of dimensionality in discontinuous time series machine data

The growing interest in artificial intelligence has led to current data-driven predictive maintenance (PdM) relying on machine learning (ML) algorithms. Although ML algorithms are useful for data-intensive analysis, research shows that their performance and reliability are reduced when high-dimensional data is used for training and testing. Raw machine data can be high-dimensional due to multi-sensor measurements and discontinuous due to the wide ranges of parameter variations during continuous sensor measurements. While standard dimension reduction methods such as principal component analysis are often applied to circumvent high-dimensionality, they are often unreliable when the data is discontinuous. This paper presents a ML-based dimension reduction framework to circumvent the challenges of high-dimensional discontinuous machine data. This framework minimizes discontinuity by clustering observations based on the dataset’s modality. The modality is identified using a kernel density estimation parameterized using a heat diffusion solution to the approximate mean integrated squared error. Then, low-dimension representations of each cluster are learned using Laplacian eigenmaps embedding. Finally, the original time sequence of observations across the low-dimensional clusters is used to re-index the observations into a continuous low-dimension feature set. We demonstrate the framework’s utility on common ML-based PdM analysis using the Commercial Modular Aero-Propulsion System Simulation dataset.

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

Remaining useful lifetime prediction via deep domain adaptation

In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to different operating conditions, fault modes and noise, distribution and feature shift exist across different domains. This shift reduces the performance of predictive models when no target observed run-to-failure data is available. To address this issue, this paper proposes a new data-driven approach for domain adaptation in prognostics using Long Short-Term Neural Networks (LSTM). We use a Domain Adversarial Neural Network (DANN) approach to adapt remaining useful life estimates to a target domain containing only sensor information. We analyse our approach using the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPPS). The results show that the proposed method can provide more reliable RUL predictions than models trained only on source data for varying operating conditions and fault modes.

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.

A Relative Entropy Weibull-SAX framework for health indices construction and health stage division in degradation modeling of multivariate time series asset data

Predictive maintenance is the monitoring of an asset’s condition over its life cycle to provide a prognosis for when maintenance is required. Prior to prognosis, an asset’s life cycle is modeled by a health indicator (HI) which is derived from sensor measurements and describes an asset’s degradation over a life cycle. Often an asset’s HI is accompanied by a health stage (HS) division, which describes the asset’s life cycle condition in discrete states. Generally, HSs are discrete representations generated from discrete state transition models, dynamic state space models, or subjectively defined thresholds, which use sensor measurements to provide a HS division related to an asset’s life cycle degradation. HS division methods are often designed for a specific asset in which HS division is based on user assumptions and not generalizable to different asset types or asset data representations. Also, HS division methods are often limited to a bi-state HS division (normal and failure), in which unobservable states are often generalized transition states. As assets become more complex and require multivariate measurements, more advanced methods are required to model an asset’s degradation using HIs and HSs. This work introduces Relative Entropy Weibull-SAX (REWS), a data-driven HI and HS degradation modeling method for multivariate asset data. REWS constructs a HI using relative entropy to represent an asset’s condition as a change of entropy during its life cycle. The relative entropy representation is then discretized into HS divisions using a Weibull distribution based Symbolic Aggregate approXimation. REWS’s utility is demonstrated on the Commercial Modular Aero-Propulsion System Simulation dataset which describes the life cycle observations of multiple aircraft engines.

A multimodal and hybrid deep neural network model for Remaining Useful Life estimation

Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge calls for an urgent quest to design advanced and innovative data-driven solutions and efficiently incorporate multi-sensor streaming data sources for condition-based maintenance. Remaining Useful Life (RUL) is a crucial measure used in this regard within manufacturing and industrial systems, and its accurate estimation enables improved decision-making for operations and maintenance. Capitalizing on the recent success of multiple-model (also referred to as hybrid or mixture of experts) deep learning techniques, the paper proposes a hybrid deep neural network framework for RUL estimation, referred to as the Hybrid Deep Neural Network Model (HDNN). The proposed HDNN framework is the first hybrid deep neural network model designed for RUL estimation that integrates two deep learning models simultaneously and in a parallel fashion. More specifically, in contrary to the majority of existing data-driven prognostic approaches for RUL estimation, which are developed based on a single deep model and can hardly maintain good generalization performance across various prognostic scenarios, the proposed HDNN framework consists of two parallel paths (one LSTM and one CNN) followed by a fully connected multilayer fusion neural network which acts as the fusion centre combining the output of the two paths to form the target RUL. The HDNN uses the LSTM path to extract temporal features while simultaneously the CNN is utilized to extract spatial features. The proposed HDNN framework is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Our comprehensive experiments and comparisons with several recently proposed RUL estimation methodologies developed based on the same data-sets show that the proposed HDNN framework significantly outperforms all its counterparts in the complicated prognostic scenarios with increased number of operating conditions and fault modes.

Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process

Remaining useful life (RUL) prediction is a key process for prognostics and health management (PHM). However, conventional model-based methods and data-driven methods for RUL prediction are bad at a very complex system with multiple components, multiple states and therefore extremely large amount of parameters. In order to solve the problem, a general two-step solution is proposed in this paper. In the first step, kernel principle component analysis (KPCA) is applied for nonlinear feature extraction. Then, a novel recurrent neural network called gated recurrent unit (GRU) is presented as the second step to predict RUL. GRU network is capable of describing a very complex system because of its specially designed structure. The effectiveness of the proposed solution for RUL prediction of a nonlinear degradation process is proved by a case study of commercial modular aero-propulsion system simulation data (C-MAPSS-Data) from NASA. Results also show that the proposed method requires less training time and has better prediction accuracy than other data-driven methods.

Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning

Degradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling methods. The objective of this study is to introduce an ensemble learning-based prognostic approach to modeling an exponential degradation process due to wear as well as predicting the RUL of aircraft engines. The ensemble learning algorithm combines multiple base learners, including random forests (RFs), classification and regression tree (CART), recurrent neural networks (RNN), autoregressive (AR) model, adaptive network-based fuzzy inference system (ANFIS), relevance vector machine (RVM), and elastic net (EN), to achieve better predictive performance. The particle swarm optimization (PSO) and sequential quadratic optimization (SQP) methods are used to determine optimum weights that are assigned to the base learners. The predictive model trained by the ensemble learning algorithm is demonstrated on the data generated by the commercial modular aero-propulsion system simulation (C-MAPSS) tool. Experimental results have shown that the ensemble learning algorithm predicts the RUL of the aircraft engines with considerable robustness as well as outperforms other prognostic methods reported in the literature.

Aircraft engine health prognostics based on logistic regression with penalization regularization and state-space-based degradation framework

Engine health prognostics is critical to ensure reliability and safety of aircraft operations due to the provision of various health decision information. In this studying, a prognostics system is developed based on logistic regression (LR) and state-space-model (SSM) for engine health assessment and prediction. In this system, a health indicator based on logistic probability (LP) inferred from a variable set of sensor signals selected by LR with penalization regularization (LRPR) is used to characterize engine health states. LP is capable of offering a failure probability for the monitored engine, which has intuitive explanation related to its health state. A data-model-fusion method is developed for the engine health prognostics task accomplished by integration of LR and particle filtering (PF). Bayesian state estimations, on the basis of the engine health changes modeled by a baseline LR, are implemented to sequentially update the current health state and then to predict the future health propagation of engines. The prognostics system is applied to a gas turbine on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) test-bed developed by NASA. The experimental results indicate the potential applications of the proposed system as an effective tool for engine health prognostics.

Hardware Development for the Controlled Fault Injection into a Turbofan Engine Air-Bleed Valve


 
 
 Gas path fault diagnostics assists operators in determining, and managing the health of gas turbine engines. Engine data depicting fault progression under realistic operating conditions is useful for the maturation of these diagnostic methods. In this paper, we present hardware created to inject a progressive fault in an air bleed valve of a high bypass turbofan engine during on-wing engine testing. The developed hardware interrupts and overrides the engine control computer’s command of the valve and allows for the nondestructive, progressive off-schedule operation of the air bleed valve. Numeri- cal simulation results based on NASA’s Commercial Modular Aero-Propulsion System Simulation 40k are presented to illustrate representative changes in measured engine parameters that can be expected during such an experiment.
 
 

A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis

Prognostics involves the effective utilization of condition or performance-based sensor signals to accurately estimate the remaining lifetime of partially degraded systems and components. The rapid development of sensor technology, has led to the use of multiple sensors to monitor the condition of an engineering system. It is therefore important to develop methodologies capable of integrating data from multiple sensors with the goal of improving the accuracy of predicting remaining lifetime. Although numerous efforts have focused on developing feature-level and decision-level fusion methodologies for prognostics, little research has targeted the development of “data-level” fusion models. In this paper, we present a methodology for constructing a composite health index for characterizing the performance of a system through the fusion of multiple degradation-based sensor data. This methodology includes data selection, data processing, and data fusion steps that lead to an improved degradation-based prognostic model. Our goal is that the composite health index provides a much better characterization of the condition of a system compared to relying solely on data from an individual sensor. Our methodology was evaluated through a case study involving a degradation dataset of an aircraft gas turbine engine that was generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines

This brief paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of quasi-stationary steady-state time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPSS) transient test-case generator.

Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements

An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e.g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes symbolic dynamic filtering by optimally partitioning the time series data of sensor observation. The objective here is to mask the effects of sensor noise level variation and magnify the system fault signatures. In this regard, the concepts of feature extraction and pattern classification are used for fault detection in aircraft gas turbine engines. The proposed methodology of data-driven fault detection is tested and validated on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) test-bed developed by NASA for noisy (i.e., increased variance) sensor signals.