bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Health Management System Approach
Authors: Mayfield Edward W. , Niu Guangxing , Zhang Bin , Ziehl Paul , Golda Michael
Published: February 2021

This paper aims to develop an integrated shipboard condition prognostics system that integrates sensing, feature extraction, and particle filtering-based diagnostic and prognostic algorithms with applications to bearing systems. The proposed effort aims to provide effective assessment of the condition of shipboard rotating machinery systems and lower the operation and maintenance (O&M) cost. The proposed work is tested on data of various fault modes, models with multiple interactive faults, and experimental testbed as a whole system. The proposed condition prognostics system is scalable, generic, easy-to-implement, and mathematically rigorous, which can be applied to a variety of Navy applications.
Concurrent Estimation of Remaining Useful Life for Multiple Faults in an Ion Etch Mill
Authors: Singh Kuldeep , Selvanathan Balaji , Zope Kalyani , Nistala Sri Harsha , Runkana Venkataramana
Published: February 2021

The 2018 PHM Data Challenge posed the problem of estimating Remaining Useful Life (RUL) for multiple faults in ion etch mills. As with any industrial system, run-to-failure data for the mills is not directly available and the mills experience more than one fault at the same time. We propose a novel data-driven methodology to address these challenges and develop a workflow that can be used for concurrent estimation of RUL for multiple faults in ion etch mills in real time. In the proposed approach, operational data of the ion etch mill is used to build a machine learning model for predicting a health score of the mill and to create a library of truncated degradation curves for each fault. These are then utilized for RUL predictions using Dynamic Time Warping (DTW) curve matching. Application of the proposed approach to test and validation datasets provided during the data challenge showed reasonable agreement between RUL predictions and the ground truth. The approach proposed here can be extended to other industrial systems and equipment for which historical operational data and failure information is available. This framework will help optimize health management and pave the way for predictive maintenance of industrial equipment.
Failure and Remaining Useful Life Prediction of Wind Turbine Gearboxes
Authors: Koukoura Soﬁa
Published: February 2021

The purpose of this project is to predict wind turbine gearbox incipient faults using a combination of condition monitoring data. It is expected to contribute in developing a robust frame-work for wind turbine gearbox component incipient failure prediction and remaining useful life estimation. It further pro-poses a solution on how to overcome the challenges of expert knowledge based systems using AI techniques. Wind turbine operation and maintenance decision making conﬁdence can be therefore increased.
Robust Estimation of Connected Automated Vehicles While Performing Cooperative Tasks in Presence of Malicious Agents
Authors: Merco Roberto
Published: February 2021

This research focuses on the identification and mitigation of malicious vehicles in cooperative tasks between automated connected vehicles. The approach aims to design estimators which employ the number and diversity of sensors that autonomous vehicles are equipped with in order to enrich the knowledge of the surrounding environment of each vehicle in the wireless communication network range. Since an event-triggered communication network is considered to increase the overall performance of the communication channels, the estimators have to be designed to take into account aperiodic and asynchronous measurements.
Aging Models and Lifetime Prediction of Thermally Aged Industrial Cable Insulation Polymers
Authors: Sriraman Aishwarya
Published: February 2021

There are over 600 miles of power cable installed in a typical nuclear power plant. Degradation due to thermal and radiation damage of cable insulation has been identified as one of the key factors that contribute to the loss of performance and ultimate failure of the cable. A critical aspect of cable health monitoring is to understand the nature of degradation and develop aging models to predict the service lifetime of the insulation. In this work, it is proposed to evaluate the effectiveness of four different modeling approaches to evaluate the aging behavior and remaining useful life of industrial-grade ethylene propylene rubber (EPR), a cable insulation material used extensively in nuclear power plants. A comparative study of the ability of these prognostic models to reliably predict the service lifetime of EPR while accounting for the presence of various inclusions and impurities in the production grade material will be conducted, to test their industrial applicability and evaluate their relative performance
Automatic Indexation of Turbofan Data to Identify Anomalous Behaviors
Authors: Lacaille Jerome , Faure Cynthia , Olteanu Madalina , Cottrell Marie
Published: February 2021

Flight data are now flowing in our databases. The fact is that we cannot analyze every single observation and we need a tool to automatically alert in case of unusual behavior and another tool to find similarities between parts of real aircraft flights. Our proposal is to systematically index the databases replacing each multivariate numerical signal acquired by the aircraft and the engine sensors by a sequence of labels. Each label should characterize a specific part of the signal such as a stationary phase or a transient phase. Stationary phases are summarized by snapshots made of statistics of the distribution parameters the multivariate signal and are easy to characterize. Transient phases are a more complex in a multivariate environment. This work apply a specific fast change detection algorithm to identify transient phases and an adaptive classification neural network to label each temporal behavior. However, as it seems natural to automatically separate standard flight phases like engine start, taxi, take-off, climb, etc. our goal is to identify different behaviors among those main classes. For example, we detect engines with slow thermal stabilization during the take-off and separate them from engines with fast thermal stabilization. We also separate hot engines during the climb phase to cold ones. The same sort of analysis is done on mechanical transfer as we may identify fast or slow crossing of specific vibration modes, etc. At the end of this segmentation and classification process, each multivariate signal is replaced by sequences of classes corresponding to context, rotation speed for example, and any endogenous observation like temperature or vibration. Then working on discrete data, it becomes easier to query the database for rare behaviors, usual behaviors, or to search some similarity with a specific engine observation. For example, looking at a specific temporal interval during a given flight it becomes possible to ask for flights and engines with similar behavior in the historic database.
Probabilistic Health and Mission Readiness Assessment at System-Level
Authors: Barbini Leonardo , Borth Michael
Published: February 2021

Predictive maintenance strategies which estimate remaining useful life of system components to prevent breakdowns and down-times by timely and well-scheduled maintenance ensures the reliable availability of assets and lowers total costs of ownership. The focus on the components’ life times falls short, however, to infer the system-level capability to achieve upcoming tasks, especially if these tasks vary either in the strain they cause for the system or in the environmental conditions in which the system needs to perform. Such an assessment of the health and mission readiness of a system is crucial for mobile assets like seafaring vessels undertaking long-term operations without the option to easily come in for repairs or for industrial assets that need to complete long production runs in one go under varying circumstances. We propose a multi-step methodology to achieve such assess­ments using both Bayesian reasoning for diagnosis and prognosis and physics-based simulation models. First, we construct an appropriate Bayesian network in an object-oriented way by fitting a pre-compiled library of network fragments to the system’s schematics using generative techniques. We then parameterize the obtained network using a combination of expert knowledge and machine learning to fine tune system-level interactions between components and their link to the system’s performance. The learning step uses past operational data that we augment or complement with synthetic data, created by a physics-based simulation model, where needed. Finally, we use the trained Bayesian network to assess the mission readiness of the system given the probabilistics of its diagnosed state, expected impact of possible maintenance interventions, and the estimated profile of the future use. We illustrate and verify our methodology on a cooling system with an active feedback control loop, but our approach for mission readiness assessment is domain-independent, universally applicable, and typically feasible where operational data and engineering knowledge can be brought together to solve its challenge.
Using Value Assessment to Drive PHM System Development in Early Design
Authors: Hulse Daniel , Hoyle Christopher , Goebel Kai , Tumer Irem
Published: February 2021

Prognostics and Health Management (PHM) systems have been shown to provide many benefits to the reliability, performance, and life of engineered systems. However, because of trade-offs between up-front design and implementation costs, operational performance, and reliability, it may not be obvious in the early design phase whether one PHM system will be more beneficial to another, or whether a PHM system will provide benefit compared to a traditional reliability approach. These trade-offs make the commitment required to pursue PHM features in the early design phase difficult to justify. In this paper, a cost model incorporating trade-offs among design cost, operational performance, and failure risk is used to provide a comprehensive value comparison of health management options to motivate design decision-making. This approach is then demonstrated in a simple case study comparing the use of a PHM system for condition-based maintenance or diagnostic-based recovery with implementing redundancy and increased inspection in the design. Then it is shown how different model inputs and assumptions result in a different system value (and different design choice from the process), illustrating the usefulness of cost modelling to capture design trade-offs. Using this approach, decisions about pursuing PHM can be made early, enabling the benefits to be fully leveraged in the design process to achieve increased operational resilience.
Categorization Errors for Data Entry in Maintenance Work-Orders
Authors: Sexton Thurston , Hodkiewicz Melinda , Brundage Michael P.
Published: February 2021

In manufacturing, there is a significant push toward the digitizationof processes and decision making, by increasing thelevel of automation and networking via cyber-physical systems,and machine learning methods that can parse usefulpatterns from these complex architectures. As such, this pushtoward being “smart” is largely driven by the availability ofdata, for analysis, decision guidance, and the training of AI.The maintenance team, however, one of the core subsystemsin any production line, remains a largely human endeavor.Consequently, the historical data needed for research and developmentof AI-assisted maintenance frameworks are oftenfull of misspellings, jargon, and abbreviations. While onemight enforce data-entry into pre-specified functional categories(generally using some form of controlled vocabulary),cognitive models, along with consistent reports from industry,indicate that data entry remains a process fraught with significanterrors, especially when a mismatch occurs between designatedschemas and the technician’s needed semantic flexibility.This paper offers a framework for understanding andaddressing these issues, with a methodological case study inapplying Human Reliability Analysis (HRA) to quantify andunderstand human errors associated with entering maintenancework-order (MWO) data into structured database (DB)schema. We subsequently suggest potential mitigation strategiesfor each to improve the quality of recorded data throughoutthe maintenance-management workflow.
Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data
Authors: Navinchandran Madhusudanan , Sharp Michael E. , Brundage Michael P. , Sexton Thurston B.
Published: February 2021

Maintenance Work Orders (MWOs) are a useful way ofrecording semi-structured information regarding maintenanceactivities in a factory or other industrial setting. Analysisof these MWOs could provide valuable insights regardingthe many facets of reliability, maintenance, and planning.Information such as which maintenance activities consumethe most work hours, identification of problem machines,and spare parts needs can all be inferred to some degreefrom well documented MWOs. However, before one canderive insights, it is first necessary to transform the data inthe MWOs (generally some form of natural language) intosomething more suitable for computer analysis. NIST previouslydeveloped a computer aided tagging system that allowsfor the quick identification of key concepts within the naturallanguage of the MWOs, and a protocol for categorizingthese concepts as solutions, problems, or items. Using thisannotation method, this paper investigates machine learningmethods to gain insights about work hours needed for variousmaintenance activities. Through these methods, it ispossible to explain the factors captured in the MWOs thathave the strongest relationship with the duration of maintenanceactions. The workflow of this research is to firstbuild strong data driven models to classify the duration ofany maintenance activity based on the language and conceptsgathered from the associated MWO. Sensitivity analysis ofthe inputs to these classifiers can then be used to determinerelationships and factors influencing maintenance activities.This paper investigates two machine learning models - aneural network classifier and a decision tree classifier. Inputfeatures for the classifier were the annotated concept tags for solutions, problems and items derived from MWOs of anactual manufacturer. This process for gaining insights can begeneralized to various applications in the maintenance andPHM communities.
Prognostics Model to Predict Brake Rotor Thickness Variation
Authors: Kazemi Hamed , Du Xinyu , Drame Samba , Dixon Regan , Sadjadi Hossein
Published: February 2021

Brake rotor thickness variation causes brake torque variation which can lead to brake judder and pulsation, steering wheel oscillations and chassis vibration. In this paper, we have proposed a prognostics methodology to predict the degradation level of brake rotor due to disc thickness variation. Leveraging the time and frequency domain analysis, this model creates health indicators to assess the health of the rotor and predict the rotor thickness variations of 36 micrometers or more. These health indicators that are calculated during braking events include: (i) envelope or variance of the brake master cylinder pressure (MCP); (ii) envelope or variance of the longitudinal acceleration (AX); (iii) the root mean square amplitude of the average order spectrum of the MCP at order one; and (iv) the root mean square amplitude of the average order spectrum of the AX at order one. This paper demonstrates that the above health indicators are significantly larger for a degraded brake rotor due to thickness variation compared to a healthy rotor.
Anomaly Detection of 2.4L Diesel Engine Using One-Class SVM with Variational Autoencoder
Authors: Jang Gyebong , Cho Sung-bae
Published: February 2021

Despite the intensive research, the study on preventing the breakdown of the construction machine is still at its early stage, so we need to develop an autonomous and robust solution that minimizes equipment downtime and ensures the rigidity of equipment through predictive diagnostics. In particular, engine failure is critical to cause the entire system to stop, so that it is important to determine and predict the symptoms before the failure. However, at present, it is at a level to set specific indicators based on domain knowledge in order to judge the failure. This paper proposes an anomaly detection model for a 2.4L diesel engine, and verify the model using two main faults. The proposed method extracts 130 feature parameters based on autoencoder, which is a deep learning method, and distinguishes between normal and abnormal states by one-class SVM (OCSVM). Autoencoder automatically extracts useful features from multiple sensors on an excavator engine. The data from the engine can represent robust features by using features learned in latent variables using variational autoencoder to extract optimal features. In addition, OCSVM can detect abnormal state and then distinguish between two fault and unknown factors. The experimental results show the accuracy of about 73%, and the false alarm related to the reliability of this abnormality diagnosis model can be minimized to about 17%. Finally, to solve the problem of reliability and analysis of the model itself due to the problem of blackbox, which is a disadvantage of the deep learning model, the LIME analysis method is applied to list the sensor data that affected the determination of the abnormal state. Experts can easily make professional judgments about abnormal conditions and build a model in which known data about faults and symptoms are continuously increasing. The proposed method could improve the accuracy of the model by adding expert knowledge to data-based model.
Wind Turbine Main Bearing Fatigue Life Estimation with Physics-informed Neural Networks
Authors: Yucesan Yigit Anil , Viana Felipe A. C.
Published: February 2021

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the design lives of the component. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics- informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (e.g., contribution due to poor greasing conditions).
Computation Method of Gear Dynamic Response Using Experimental Strain Data and Application in Pitting Fault Analysis
Authors: Qu Yongzhi , Zhang Haoliang , Wang Zechao , Zhou Zude
Published: February 2021

In this paper, A semi-physical method for calculating time varying mesh stiffness and the dynamic response of gear system based on experimental strain data is studied. In a previous work, it was reported that dynamic strain on gear tooth root can be measured under normal operating condition using fiber Bragg Grating (FBG) sensors. This paper aims to compute gear dynamic response using experimental strain data and give an explanation of the fault propagation process. Using the dynamic strain data from FBG sensors, a method for calculating the dynamic response of gear system is proposed. Based on the theory of potential energy and material mechanics, the relationship between the bending strain of the tooth root and the time varying mesh stiffness is established. The time varying mesh stiffness and dynamic response of healthy gear and pitted gear are then calculated respectively. The force transmission during gear mesh under the condition of surface pitting is analyzed. It is concluded that in the case of pitting fault, there will be a significant loss of torque in the power transmission process due to the loss of contact area. It is further inferred that the loss of meshing force andthedecreasing of Hertzian contact stiffness are the major contributing factors for pitting fault. In addition, the semi-analytical method of computing gear dynamic response is validated with experimental study ofacceleration signal in the perspective of dynamic response.
Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals
Authors: Jung Daniel
Published: February 2021

Localization of unknown faults in industrial systems is a dif- ficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example faults not represented in training data, can be detected using, for example, anomaly classifiers. However, mapping these unknown faults to an actual location in the real system is a non-trivial problem. In model-based diagnosis, physical-based models are used to create residuals that isolate faults by mapping model equations to faulty system components. De- veloping sufficiently accurate physical-based models can be a time-consuming process. Hybrid modeling methods combining physical-based methods and machine learning is one solu- tion to design data-driven residuals for fault isolation. In this work, a set of neural network-based residuals are designed by incorporating physical insights about the system behavior in the residual model structure. The residuals are trained using only fault-free data and a simulation case study shows that they can be used to perform fault isolation and localization of unknown faults in the system.
Isolation-based feature Selection for Unsupervised Outlier Detection
Authors: Yang Qibo , Singh Jaskaran , Lee Jay
Published: February 2021

For high-dimensional datasets, bad features and complex interactions between features can cause high computational costs and make outlier detection algorithms inefficient. Most feature selection methods are designed for supervised classification and regression, and limited works are specifically for unsupervised outlier detection. This paper proposes a novel isolation-based feature selection (IBFS) method for unsupervised outlier detection. It is based on the training process of isolation forest. When a point of a feature is used to split the data, the imbalanced distribution of split data is measured and used to quantify how strong this feature can detect outliers. We also compare the proposed method with variance, Laplacian score and kurtosis. These methods are benchmarked on simulated data to show their characteristics. Then we evaluate the performance using one-class support vector machine, isolation forest and local outlier factor on several real-word datasets. The evaluation results show that the proposed method can improve the performance of isolation forest, and its results are similar to and sometimes better than another useful outlier indicator: kurtosis, which demonstrate the effectiveness of the proposed method. We also notice that sometimes variance and Laplacian score has similar performance on the datasets.
Study on PHM Method Suitable for Motor-Driven Commercial Vehicle
Authors: Chang Hong Suk , Park Jong Chan
Published: February 2021

Commercial vehicles need to use PHM because the drivers cannot earn money during that period when a breakdown occurs. However, PHM technology has been actively applied in aviation and military, but it is also true that it has never been properly applied due to various limitations in automobile industry. In this study, PHM methodology suitable for commercial vehicle is constructed.

In the first step, the type, number, and position of the sensors are selected by applying the optimization technique so that the failure mode can be detected with considering cost reduction. Second, in case of a single vehicle such as aviation or military, PHM could be applied by using only data acquisition. However, in the case of commercial vehicles with several derivative cars, a hybrid model that considers the analytical model and data acquisition was applied to construct a diagnosis and prognostic model. Lastly, until now data driven approach was a way to check the status with the data obtained from the sensor and to report the remaining useful life when it continued. In advanced, in the vehicle, a dynamic model should be used to take into account external environment. And the SOFT SENSOR methodology that can catch the state of a specific weak point by using the detailed analysis model of NVH, thermal, and strength has been constructed.
A Comparative Study on Computation of Cumulative Distribution Function in Predicting Time of Failure of Engineering Systems
Authors: Sierra Paez Gina Katherine , Daigle Matthew , Goebel Kai
Published: February 2021

Estimating accurate Time-of-Failure (ToF) of a system is key in making the decisions that impact operational safety and optimize cost. In this context, it is interesting to note that different approaches have been explored to tackle the problem of estimating ToF. The difference is in part characterized by different definitions of the hazard zones. The conventional definition for the cumulative distribution function (CDF) calculation is assumed to have well-defined hazard zones, that is, hazard zones defined as a function of the system state trajectory. An alternate method suggests the use of hazard zones defined as a function of the system state at time , instead of hazard zones defined as a function of system state up to and including time k (Acuña and Orchard 2018, 2017). This paper explores these differences and their impact on ToF estimation. Results for the conventional CDF definition indicated that, (i) the cumulative distribution function is always an increasing function of time, even when realizations of the degradation process are not monotonic, (ii) the sum of all probabilities is always 1 and does not need to be normalized, and (iii) all probabilities are positive and less than or equal to 1. Similar results are not observed for CDF calculation with hazard zones defined as a function only of the system state at time k. Results for ToF estimation using Acuña's definition differ, suggesting that there is an underlying assumption of independence in the hazard zone definition.  Therefore, we present an alternate definition of hazard zone which guarantees the properties of a well-defined CDF with a more straightforward ToF definition.
Aircraft Trajectory Prediction using LSTM Neural Network with Embedded Convolutional Layer
Authors: Pang Yutian , Xu Nan , Liu Yongming
Published: February 2021

The development of convective weather avoidance algorithm is crucial for aviation operations and it is also a key objective of the next generation air traffic management system. This paper proposes a novel network architecture that embeds convolutional layers into long short-time memory (LSTM) cells to predict the trajectory, based on the convective weather condition along the flight plan before the aircraft takeoff. The data used in the experiments are history flight track data, the last on-file flight plan, and the time-dependent convective weather map. The history flight data are taken from NASA Sherlock database and the weather data used in this paper is the Echo Top (ET) convective weather product from Corridor Integrated Weather System (CIWS). The experiment is conducted using three months history data over the period from Nov 1, 2018 through Feb 5, 2019 with the flights from John F. Kennedy International Airport (JFK) to Los Angeles International Airport (LAX) but the methodology can be applied to the flights between any arbitrary two airports. Interpolation is performed on flight plans and real history tracks to fix the fold number of LSTM cell and also reduce computation complexity. The training loss is defined as the standard Mean Squared Error (MSE) of the predicted tracks and the real history tracks. Adam optimizer is used for backpropagation. Learning from the real historical flight data, the out-of-sample test shows that 47.0\% of the predicted flight tracks are able to reduce the deviation compared to the last on-file flight plan. The overall variance is reduced by 12.3\%.
Spatio-temporal Anomaly Detection, Diagnostics, and Prediction of the Air-traffic Trajectory Deviation using the Convective Weather
Authors: Zhao Xinyu , Yan Hao , Li Jing , Pang Yutian , Liu Yongming
Published: February 2021

With ahead-of-time aircraft management, we are able to reduce aircraft collision and improve air traffic capacity. However, there are various impact factors which will cause a large deviation between the actual flight and the original flight plan. Such uncertainty will result in an inappropriate decision for flight management. In order to solve this problem, most of the existing research attempt to build up a stochastic trajectory prediction model to capture the influence of the weather. However, the complexity of the weather information and various human factors make it hard to build up an accurate trajectory prediction framework. Our approach considers the problem of trajectory deviation as the "anomaly" and builds up an analytics pipeline for anomaly detection, anomaly diagnostics, and anomaly prediction. For anomaly detection, we propose to apply the CUSUM chart to detect the abnormal trajectory point which differs from the flight plan. For anomaly diagnostics, we would like to link the entire anomalous trajectory sequences with the convective weather data and identify the important weather impact factors base on XGBoost and time-series feature engineering. For anomaly prediction, we will build up a point-wise prediction framework based on the Hidden Markov Model and Convectional LSTM to predict the probability that the pilot would deviate from the flight plan. Finally, we demonstrate the significance of the proposed method using real flight data from JFK to LAX.
Model-based On-board Decision Making for Autonomous Aircraft
Authors: Schumann Johann Martin , Mahadevan Nagabhushan , Lowry Michael , Karsai Gabor
Published: February 2021

Powerful, small and lightweight sensors in combination with advanced failure detection, diagnosis, and prognostics techniques provide up-to-date data on the health status of a Unmanned Aerial System (UAS). In an autonomous UAS, this information must be used for automatic planning and execution of contingency actions to keep the UAS safe in adverse conditions.We present DM (Decision Maker), a software component which uses model-based reasoning, backtracking search to iteratively construct contingency plansthat are safe for the UAS to execute and pose minimal interruption to the mission goals. The DM, which has been developed within the NASA Autonomous Operating System (AOS) project thus fills the gap between Prognostics and Health Management and autonomous flight operations.In this paper, we describe DM and its reasoning/search algorithm and present the supporting modeling framework for the construction of system and fault models. An flight with a DJI S1000+ octocopter with fault injection will be used as our case study.
Adversarial Campaign Mitigation via ROC-Centric Prognostics
Authors: Echauz Javier , Kenemer Keith , Hussein Sarfaraz , Dhaliwal Jay , Shintre Saurabh , Grzonkowski Slawomir , Gardner Andrew
Published: February 2021

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alchemy, with no practical unbroken defenses demonstrated to date, leaving PHM practitioners with few meaningful ways of addressing the problem. We introduce “turbidity detection” as a practical superset of the adversarial input detection problem, coping with adversarial campaigns rather than statistically invisible one-offs. This perspective is coupled with ROC-theoretic design guidance that prescribes an inexpensive domain adaptation layer at the output of a deep learning model during an attack campaign. The result aims to approximate the Bayes optimal mitigation that ameliorates the detection model’s degraded health. A proactively reactive type of prognostics is achieved via Monte Carlo simulation of various adversarial campaign scenarios, by sampling from the model’s own turbidity distribution to quickly deploy the correct mitigation during a real-world campaign.
Automating Visual Inspection with Convolutional Neural Networks
Authors: Das Sreerupa , Hollander Christopher D , Suliman Suraiya
Published: February 2021

Convolutional Neural Networks (CNNs) have become the recent tool of choice for many visual detection tasks, including object classification, localization, detection, and segmentation. CNNs are specialized neural networks composed of many layers and specifically designed to analyze grid-like data, e.g. images. One of the key features of a CNN is its ability to automatically detect important features within an image (e.g. edges, patterns, shapes); prior to CNNs, these features had to be manually engineered by subject matter experts.

Inspired by the significant achievements and success that CNNs have experienced in the domain of computer vision, we examine a specific convolutional neural network (CNN) architecture, U-Net, suited for the task of visual defect detection. We identify and discuss situations for the use of this architecture in the specific context of external defect detection on aircraft and experimentally discuss its performance across a dataset of common visual defects.

One requirement of training Convolution Networks on an image analysis task is the need for a large image (training) data set.  We address this problem by using synthetically generated images from computer models of jets with varying angles and perspectives with and without induced faults in the generated images.  This paper presents the initial results of using CNNs, specifically U-Net, to detect aerial vehicle surface defects of three categories.  We further demonstrate that CNNs trained on synthetic images can then be used to detect faults in real images of jets with visual damages.  The results obtained in this research, indicate that our approach has been quite effective in detecting surface anomalies in our tests.
Health Monitoring Framework for Aircraft Engine System using Deep Neural Network
Authors: Lee Hyunseong , Li Guoyi , Rai Ashwin , Chattopadhyay Aditi
Published: February 2021

A real-time monitoring framework is developed to detect operational anomalies in aircraft engine performance. A historical flight dataset recorded from commercial aircraft is utilized to perform the proposed method. Sampling frequency synchronization and denoise are performed on the flight dataset using signal processing techniques. A robust detection algorithm using the deep neural network is developed to capture flight performance anomalies that show significant off-nominal behavior in engine related and flight dynamic features. The accuracy and efficiency of the proposed monitoring method are validated through a demonstration of anomaly detection in the aircraft engine system associated with dynamic flight behavior.
Rolling Element Bearing Fault Diagnosis Based on Deep Belief Network and Principal Component Analysis
Authors: Niu Guangxing , Zhang Bin , Ziehl Paul , Ferrese Frank , Golda Michael
Published: February 2021

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Bearing fault diagnosis is therefore essential and significant to safe and reliable operation of systems. For bearing condition monitoring, acoustic emission (AE) signals attract more and more attention due to its advantages on sensitivity over the extensively used vibration signal. In bearing fault diagnosis and prognosis, feature extraction is a critical and tough work, which always involves complex signal processing and computation. Moreover, features greatly rely on the characteristics, operating conditions, and type of data. With consideration of changes in operating conditions and increase of data complexity, traditional diagnosis approaches are insufficient in feature extraction and fault diagnosis. To address this problem, this paper proposes a Deep Belief Network (DBN) and Principal Component Analysis (PCA) based fault diagnosis approach using AE signal. This proposed approach combines the advantages of deep learning and statistical analysis, DBN automatically extracts features from AE signal, PCA is applied to dimensionality reduction. Different bearing fault modes are identified by least squares support vector machine (LS-SVM) using the extracted features. An experimental case is conducted with a tapered roller bearing to verify the proposed approach. Experimental results demonstrate that the proposed approach has excellent feature extraction ability and high fault classification accuracy.
Progress towards a Framework for Aerospace Vehicle Reasoning (FAVER)
Authors: Ezhilarasu Cordelia Mattuvarkuzhali , Skaf Zakwan , Jennions Ian K
Published: February 2021

This paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the component and subsystem levels; only a few IVHM systems consider the interaction between the systems. To diagnose faults at the vehicle level, an IVHM System needs a framework that recognizes the causal relationships between systems and the likelihood of fault propagation between them. The framework should also possess an element of reasoning to assess data from all systems, to assign priorities, and to resolve ambiguities. The Framework for Aerospace VEhicle Reasoning (FAVER) that is proposed in this paper uses a digital twin of the aircraft systems to emulate functioning of the aircraft and to simulate the effect of fault propagation due to systems interactions. FAVER applies reasoning that can handle fault signatures from multiple systems in the form of symptom vectors, to detect and isolate cascading faults and their root causes. The blending of a digital twin and reasoning in this framework will enable FAVER to: i) isolate faults that have both local and cascading effects on the concerned systems, ii) identify faults that were previously unknown, and iii) resolve ambiguous faults. This paper explains the different steps involved in developing FAVER and how this framework can be demonstrated in the aforementioned scenarios with the help of different use cases. This paper also talks about the challenges to be faced while developing this framework and ways to overcome them.
Survey of Flight Anomaly Detection Methods: Challenges and Opportunities
Authors: Igenewari Vivian Rowoli , Skaf Zakwan , Jennions Ian K.
Published: February 2021

Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel. There is also the need to prevent fatalities, increase reliability and reduce monetary costs suffered as a result of delays and accidents that still occur. Accidents today are complex as a result of many causal factors acting alone but more often as a combination with other contributing factors. In tackling this trend, proactive measures have been put in place to find hazardous combinations that occur during flights in order to mitigate them before accidents occur. Flight Anomaly Detection (AD) methods are aimed at highlighting abnormal occurrences of a flight, that are different from the norm. As an improvement on the current state-of-the-art method, previous works have proposed different AD techniques for detection of previously unknown flight risks such as component faults, aircraft operational inefficiencies and some abnormal crew behaviour. However, current AD methods individually have limitations that prevent them from detecting certain significant anomalies in flight data. This paper surveys current flight AD approaches, their strengths and limitations as well as brings to light the benefits of a hybrid AD method to extend previous work and find safety-critical events, particularly those related to abnormal crew activity: a class of events known to amount for a substantial number of accidents/incidents today. It also highlights another emerging AD application opportunity, its challenges and how AD is beneficial in addressing them.
An Integrated model-based Approach for FMECA Development for Smart Manufacturing Applications
Authors: Ghoshal Sudipto , Deb Somnath , Haste Deepak , Hess Andrew , Zahiri Feraidoon , Sutton Gregory
Published: February 2021

Qualtech Systems, Inc. (QSI)’s integrated tool set, consisting of TEAMS-Designer® and TEAMS-RDS® provides a comprehensive model-based systems engineering approach that can be deployed for fault management throughout the equipment life-cycle – from its design for fault management to condition-based maintenance of the equipment. The TEAMS® failure-cause effect dependency model is a digital twin representation of the equipment in its failure-space and allows for various types of analyses such as testability, serviceability, failure propagation and others that facilitate fault management design of the equipment. The same model is deployed through TEAMS-RDS® for condition monitoring, prognostics, real-time health assessment, failure impact analysis, guided troubleshooting and others that facilitate condition-based maintenance as well as ensure efficient and rapid maintenance actions. In this paper, we present an overview of QSI’s integrated toolset, with a focus on a systematic model-based approach towards an automated development of Failure Effects and Criticality Analysis (FMECA) and other relevant analyses for the equipment, for an improved understanding of failure effects and their causality at the system-level. The eventual objective here is improved equipment design as well as designing improved failure detection, failure isolation and failure mitigation. The paper will also discuss examples of such real-world applications for smart manufacturing in major depot maintenance facilities in the US. A subsequent paper will focus on the development and integration of process-level and equipment-level FMECAs for Smart Manufacturing applications.
Framework for Resilience Informed Decision Making in Early Design
Authors: Hulse Daniel
Published: February 2021

Early in the design process, informed decisions must be made to ensure that the developed system will be resilient—that is, capable of preventing, mitigating, or recovering failures. However, at this phase of design, many options exist to achieve resilience, each with different effects on the system’s fault response, performance, and difficulty to implement. As a result, it is important to be able to quantify the value of a design’s resilience so that it can be traded off against these other concerns. Advancements in the capabilities of Prognostics and Health Management, fault-tolerant control and related technologies have enabled a variety of novel prevention and recovery features that require an understanding of the system’s structure and available functions during operation to consider properly. This work aims to develop modelling and design frameworks enabling the consideration of these features, such as system reconfiguration, functional redundancy, operational failure avoidance, and goal change early in the design process. Such design frameworks will show which designed features are most appropriate in the system and will account for the uncertainty of assumptions made in early design phase.
Framework to Interpret Deep Learning-Based Health Management System with Human Interactions
Authors: Azarian Michael H. , Lee Namkyoung , Pecht Michael G.
Published: February 2021

Deep learning has shown good performance in detecting a product’s faults and estimating the remaining useful life of a product. However, it is hard to interpret deep learning-based health management systems because deep learning is often regarded as a black box. In order to make a maintenance decision based on the result of the management system, humans need to know how it gave the outcome. This study aims to develop a framework that utilizes human interactions during system development to understand the internal process of deep learning. The study will demonstrate the framework on bearing datasets.
Multimodality Information Fusion for Aging Pipe Strength and Toughness Estimation Using Bayesian Networks
Authors: Liu Yongming , Chen Jie
Published: February 2021

Accurate estimation of the mechanical property of aging pipes is critical to maintain the safety and to scheduling maintenance. Destructive testing for mechanical properties measurement is very expensive and sometime impossible. Inference methods are needed for estimating the bulk properties by multimodality surface material measurements from nondestructive testing, such as chemical composition, volume fraction and hardness. Bayesian network modeling is utilized to integrate the information from various types of surface measurements for a more accurate bulk mechanical property estimation. To improve the approximation of the actual underlying model and avoid the risk of overfitting, Bayesian model averaging (BMA) of Bayesian networks is implemented to account for Bayesian network model uncertainty. The models considered are weighted based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. The predictive performance of single best model and BMA are compared by logarithmic scoring rule. The predictive capability of the proposed method is evaluated. It is shown that the Bayesian network and model averaging approach can provide more reliable results in predicting the bulk mechanical properties of the pipelines.
Enhancing Railway Pantograph Carbon Strip Prognostics with Data Blending through a Time-Delay Neural Network Ensemble
Authors: Trilla Alexandre , Fern´andez Ver´onica , Cabr´e Xavier
Published: February 2021

Energy supply for high-speed trains is mainly attained witha high-voltage catenary (i.e., the source on the infrastructure)in contact with a sliding pantograph (i.e., the drain on therolling-stock vehicle). The friction between these two elementsis minimised with a carbon strip that the pantographequips. In addition to erosion, this carbon strip is also subjectto abrasion due to the high current that flows from the catenaryto the train. Therefore, it is of utmost importance to keepthe degradation of the carbon material under control to guaranteethe reliability of the railway service. To attain this goal,this article explores an accurate (i.e., uncertainty bounded)predictive method based on a robust online non-linear multivariateregression technique, considering some factors thatmay have an impact on the degradation on the carbon strip,such as the seasonal condition of the contact wire, which maydevelop an especially critical ice build-up in the winter. Theproposed approach uses a neural ensemble to integrate allthese sources of potential utility with the carbon strip data,which is convoluted in time with a set of spreading filters toincrease the overall robustness. Finally, the article evaluatesthe effectiveness of this prognosis approach with a dataset ofpantograph carbon thickness measurements over a year at thefleet level. The results of the analysis prove that it is definitelypossible to deploy a fine prediction, and thus yield a new avenuefor business improvement through the application of thepredictive maintenance approach to pantograph carbon strips.