bluuebunny/crossref_metadata_2025 huggingface dataset:
Constrained Blind Source Separation by Morphological Characteristics and Its Application in Modal Analysis
Authors: Gong Teng , Zhang Zhousuo , Wang Huan
Published: February 2021

Semi-blind source separation algorithm is widely concerned for its advantages over classical blind source separation algorithm. However, in practical applications, it is often a difficult problem to design reference signals, which should be closely related to the desired source signals. Therefore the algorithm of constrained blind source separation by morphological characteristics is proposed in this paper, including three steps: the establishment of the enhanced contrast function, the optimization calculation and the extraction of multiple source signals. Firstly, the indexes measuring the morphological characteristics of a source signal are constructed based on the known prior information and introduced into the traditional contrast function to establish an enhanced contrast function, extending the use of prior information. Then, the optimization calculation is accomplished by genetic algorithm, obtaining a single source signal. Finally, the extraction of multiple source signals is realized by cluster analysis. The proposed algorithm is applied to the modal analysis under random excitation. The spectrum symmetry index is constructed and introduced into the kurtosis contrast function to establish the enhanced contrast function, thus realizing the extraction of each signal modal response. The extraction results show the effectiveness and superiority of the algorithm.
Rolling Bearing Diagnosis Based on CNN-LSTM and Various Condition Dataset
Authors: Yoshimatsu Osamu , Satou Yoshihiro , Shibasaki Kenichi
Published: February 2021

Flaking is typical failure mode in rolling bearings. Therefore, flaking diagnosis plays a critical role in condition monitoring of general rotating machinery. In recent years, there has been an increasing interest in deep learning technique for bearing flaking diagnosis, because it can learn the flaking induced vibration features with no information of bearing specifications nor that of rotating speed. However, most of the studies have only focused on laboratory data using one test rig as well as a small dataset under the limited operating condition. Accordingly, no discussion has been found on the generalization performance of the diagnostic model, i.e., availability for actual rotating machinery, in which vibration feature is affected by various operating conditions and unknown disturbance. In this study, more than 21,000 timeseries waveforms of normal and bearing flaking induced machine vibration were prepared from three types of test rig and three bearing types under various operating condition. And deep learning such as Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) models were applied to recognize flaking bearing vibration. The applied models trained with various condition data showed higher accuracy of various condition test data diagnosis than other models trained using single condition data. Furthermore, the applied diagnostic models also showed less accuracy degradation for test data in which additional artificial noise was imposed, than the models trained with single condition data.
Rapid Uncertainty Propagation for High-Fidelity Prognostics Using SROMPy and Python
Authors: Warner James E. , Leser Patrick E. , Hochhalter Jacob D.
Published: February 2021

This work introduces a practical approach for accelerating probabilistic, high-fidelity prognostics using the stochastic reduced order model (SROM) method and its availability in the open-source Python package, SROMPy. SROMs are used as an efficient Monte Carlo simulation (MCS) method, providing low-dimensional representations of random model inputs enabling rapid and non-intrusive uncertainty propagation. This study represents the first application of the SROM approach in the field of prognostics and health management and serves as a tutorial demonstration of the SROMPy software package. The relative ease of applying SROMs with SROMPy for uncertainty propagation is demonstrated on an example of probabilistic, non-planar crack growth simulation. Results show that the SROM approach agrees well with results from MCS while providing orders of magnitude computational speedup. The complete source code and input data required to reproduce the results in this paper are available online to facilitate further evaluation and adoption of the SROM method by researchers in the field.
Sequential Monte Carlo: Enabling Real-time and High-fidelity Prognostics
Authors: Leser Patrick E. , Hochhalter Jacob D. , Warner James E , Bomarito Geoffrey F. , Leser William P. , Yuan Fuh-Gwo
Published: February 2021

Uncertainty quantification and propagation form the foundation of a prognostics and health management (PHM) system. Particle filters have proven to be a valuable tool for this reason but are generally restricted to state-space damage models and lack a natural approach for quantifying model parameter uncertainty. Both of these issues tend to inhibit the real-world application of PHM. While Markov chain Monte Carlo (MCMC) sampling methods avoid some of these restrictions, they are also inherently serial, and, thus, MCMC can become intractable as model fidelity increases. Over the past two decades, sequential Monte Carlo (SMC) methods, of which the particle filter is a special case, have been adapted to sample from a single, static posterior distribution, eliminating the state-space requirement and providing an alternative to MCMC. Additionally, SMC samplers of this type can be run in parallel, resulting in drastic reductions in computation time. In this work, a potential path toward real-time, highfidelity prognostics using a combination of surrogate modeling and a parallel SMC sampler is explored. The use of SMC samplers to enable tractable parameter estimation for full-fidelity (i.e., non-surrogate-assisted) damage models is also discussed. Both of these topics are studied in the context of fatigue crack growth in a geometrically complex, metallic specimen subjected to variable amplitude loading.
Developing a Knowledge Management System for Integrated Vehicle Health Management Using a Data Life Cycle Model
Authors: Maindze Alexslis
Published: February 2021

Data forms the foundation on which knowledge is created, captured, used and shared. The lack of an approach consistent with technological changes and needs can facilitate loss of knowledge and increased costs. Integrated Vehicle Health Management (IVHM) is characterized by prognostics and diagnostics which depend heavily on high quality data to perform data-driven, model-based and hybrid computational analysis of asset health. As a result, managing data and knowledge for Integrated Vehicle Health Management (IVHM) requires a data life cycle model that adopts the OSA-CBM data model and integrate with other approaches. This project will propose such a model and use it to support the development of an IVHM knowledge management system.
Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain
Authors: Wang Teng , Liu Zheng , Lu Guoliang
Published: February 2021

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detectionin the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.
Fault Detection of a Reusable Rocket Engine using Phase Plane Trajectory of Feature Vectors
Authors: Tsutsumi Seiji , Hirabayashi Miki , Sato Daiwa , Abe Masaharu , Kawatsu Kaname , Sato Masaki , Kimura Toshiya , Hashimoto Tomoyuki
Published: February 2021

A bivariate time-series analysis based on the phase plane trajectory of feature vectors extracted by principal component analysis is developed for fault detection in a reusable liquid-propellant rocket engine. Static-firing test results of the reusable rocket engine obtained at the Japan Aerospace Exploration Agency are employed for demonstration of the present method. The present method successfully detected temperature sensor failure from 19 firing tests of 62 sensors, even in the deviation of the engine operational sequence between the static-firing tests. The present method was also able to detect the system failure from 23 firing tests. Furthermore, the ability to distinguish the system and sensor failure was demonstrated.
State-of-Health-Oriented Power Management Strategy for Multi-Source Electric Vehicles Considering Situation-Based Optimized Solutions in Real-Time
Authors: Ali Ahmed Mohamed , Moulik Bedatri , Beganovic Nejra , Söffker Dirk
Published: February 2021

This paper presents a novel situation-based power and battery health management strategy for fuel cell vehicles. In such hybrid powertrains, the synergy role of batteries is essential to minimize overall power consumption and maintain higher electric efficiency of the fuel cell. On the other side, lifetime degradation of the battery is associated to the recurrent charging/discharging cycles. The proposed power management strategy addresses the trade-off between these contradictive objectives. Vehicle states in each situation are defined in terms of driver-related identification parameters (power demand and speed) corporately with powertrain related ones (on-board battery’s state of charge). Optimal power handling solution for each situation is searched offline considering different optimizations criteria: range extension, lifetime maximization, or power consumption minimization. A weighted fusion of these optimized solutions can be implemented online based on desired driving strategy, leading to situation-based optimized solution. This contribution aims to provide flexible power handling options meeting performance requirements (energy efficiency and driveability) without scarifying battery’s lifetime. Simulation tests using different driving cycles are conducted for evaluation purpose.
A Novel Similarity-based Method for Remaining Useful Life Prediction Using Kernel Two Sample Test
Authors: Jia Xiaodong , Cai Haoshu , Hsu Yuanming , Li Wenzhe , Feng Jianshe , Lee Jay
Published: February 2021

When abundant historical Run-to-Failure (R2F) data is available, the similarity-based method is one of the favored options for Remaining Useful Life (RUL) prediction due to its simplicity and satisfactory accuracy. In this study, a novel similarity-based methodology for RUL prediction is proposed. The proposed method has two important processing steps: similarity matching and Weibull fitting. The similarity matching screens the historical records by a similarity testing called Kernel Two Sample Test (KTST), and only those records that pass KTST are adopted as references for RUL prediction. For the selected similar records, the RUL is predicted as the remaining time to failure. The Weibull fitting fuses the multiple RUL predictions given by similar historical records. The PDF of RUL is estimated as the fitted Weibull distribution. To demonstrate the effectiveness and superiority of the proposed method, the famous CMAPSS data about aero-engine degradation is adopted for model validation. The results demonstrate improved prediction accuracy comparing with other similarity-based approaches and the state-of-the-art deep learning predictors.
Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis
Authors: Dourado Arinan , Viana Felipe A. C.
Published: February 2021

In this paper, we present a novel physics-informed neural network modeling approach for corrosion-fatigue. The hybrid approach is 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 to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion). A numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consists of predicting corrosion-fatigue of an Al 2024-T3 alloy used on panels of aircraft wing. Besides cyclic loading, the panels are also subjected to saline corrosion. The physics-informed neural network is trained using full observation of inputs (far-field loads, stress ratio and a corrosivity index – defined per airport) and very limited observation of outputs (crack length at inspection for only a small portion of the fleet). Results show that the physics-informed neural network is able to learn the correction in the original fatigue model due to corrosion and predictions are accurate enough for ranking damage in different airplanes in the fleet (which can be used to prioritizing inspection).
Experimental Results of Acoustic Emission Attenuation Due toWave Propagation in Composites
Authors: Wirtz Sebastian Felix , Bach Stefan , Söffker Dirk
Published: February 2021

Recently, acoustic emission-based damage classification schemes gained attention for health monitoring of composites. Here, the reliable detection of different micro-mechanical damage mechanisms is important because of the adverse effect on fatigue life. It is well known that classical parameters obtained from acoustic emission measurements in time domain are strongly dependent on the propagation path and testing conditions. However, signal attenuation, which can be observed due to geometric spreading, material-related damping, and dispersion, is typically neglected. Therefore, it is generally assumed that frequency domain features are reliable descriptors of damage due to invariance of peak frequencies to the propagation path. Based on this assumption, several data-driven approaches for damage detection are developed. However, in contrast to metallic materials, where low attenuation is observed, acoustic emission signals are strongly attenuated in polymer matrix composites due to viscoelastic behavior of the matrix. For instance, it is reported in literature that at high frequencies most of the acoustic emission signal energy is attenuated after a propagation distance of 250~mm. Therefore, new experimental results of acoustic emission attenuation in composites are presented in this paper. Particular focus is placed on the frequency dependence of acoustic emission attenuation and the effect of different loading conditions. The specimens are manufactured from aerospace material. Carbon fiber reinforced polymer plates are used as a typical specimen geometry. Different acoustic emission sources are considered and the related attenuation coefficients are determined. Furthermore, full waveform data are analyzed in time and time-frequency domain using wavelet transform. From the experimental results it can be concluded that consideration of wave propagation-related signal attenuation is important for the interpretation of acoustic emission measurements for health monitoring of composites. Consequently, the impact on the detectability of different physical damage mechanisms using data-driven classification approaches has to be considered.
A Comprehensive Analysis of the Performance of Gear Fault Detection Algorithms
Authors: Bechhoefer Eric , Butterworth Brent
Published: February 2021

Modern gear fault detection analysis began with algorithms based on the time synchronous average. Over the course of decades, many gear analyses have been proposed, but with no evidence that the analysis was significantly more powerful in terms of fault detection than existing algorithms.  This study focuses on a comprehensive comparison of gear fault detection algorithms to evaluate their performance. Using a large, statistically significant set of data from three nominal machines and a damaged machine, the CI responses of 88 different analysis are compared in terms of their statistical significance to detect a cracked tooth. The comparison includes residual, energy operator (and its variants), the narrowband analysis (with a comparison of bandwidth requirements), the amplitude and frequency modulation analysis, an analysis of variance of the "factor” analysis: crest, shape, impulse, and margin, and other standard gear fault CIs. Further, the effect of CI selection in the establishment of gear component health is evaluated, where given a set of CIs, a gear health indicator is built, showing that CIs with high statistically separability and low correlation have improved fault detection power. This is validated on a third, dissimilar gear fault propagation test.
Accurate Estimation of Battery SOH and RUL Based on a Progressive LSTM with a Time Compensated Entropy Index
Authors: Bak Taejun , Lee Sukhan
Published: February 2021

The accurate estimation of the State of Health (SOH) and Remaining Useful Life (RUL) has been a subject of keen interest due to its impact on safety and condition-based maintenance services. A number of approaches have been proposed to tackle this problem based either on a model-driven or on a data-driven framework. Due to the electro-chemical complexity involved in battery aging, they are yet to achieve the accuracy required, especially, for real-world applications. This is because of the difficulty either in identifying the time-varying nature of model parameters and in collecting the real-world training dataset from widely varying modes of battery usage.

In this paper, we propose a method of estimating SOH and RUL simultaneously in such a way as to contribute to its real-world applicability. First, noticing that battery aging causes the time sequence of charging and discharging voltage and current in a cycle to be shortened and dispersed, we define an aging index, referred to here as the time compensated entropy, for SOH and RUL. Second, for LSTM-based RUL prediction, we optimize the number of SOH input and the RUL prediction sequences for the minimum prediction error associated with a sequence of cycles. Third, we adopt a progressive framework of LSTMs such that whatever learned from the prior predictions are transferred to the subsequent prediction, starting with learned SOH. For experimental verification, we train the proposed progressive LSTM network based on CALCE datasets and apply to various cases of charging and discharging cycles. With SOH estimated online, we achieve less than 10 cycles of accuracy in RUL prediction, moving closer to real-world applicability.
Best Practices Framework for Improving Maintenance Data Quality to Enable Asset Performance Analytics
Authors: Lukens Sarah , Naik Manjish , Saetia Kittipong , Hu Xiaohui
Published: February 2021

Field maintenance data is often captured manually and is prone to have incomplete and inaccurate information in the structured fields.  However, unstructured fields captured through work order planning, scheduling, and execution contains a wealth of historical information about asset performance, failure patterns, and maintenance strategies.  The prevalent data quality issues in maintenance data need to be understood and processed in order to extract actionable intelligence.  This paper describes a best practices framework for measuring and improving data quality, developed through years of research and working with 120+ process and manufacturing organizations.  The framework enables evaluating and executing analytics by identifying strengths in the data.  It determines where and how asset performance measures such as benchmarking metrics, reliability measures, and bad actor identification can be evaluated with confidence.  Missing or inconsistent information can be extracted from the unstructured fields using natural language processing (NLP) techniques to bridge gaps in the analysis.  While the NLP algorithms make historical data usable for some analytics, the best practices identify improvements in the work process of capturing data, thereby improving future quality.  A feedback on data quality indicators completes the loop to sustain improvements.
Adaptive Machine Learning Approach for Fault Prognostics based on Normal Conditions - Application to Shaft Bearings of Wind Turbine
Authors: Abid Koceila , Sayed-Mouchaweh Moamar , Laurence Cornez
Published: February 2021

Prognostics can enhance the reliability and availability of industrial systems while reducing unscheduled faults and maintenance cost. In real industrial systems, data collected from the normal operation conditions of system is available, but there is a lack of historical degradation data is often unavailable. Hence, this paper proposes a general data-driven prognostic approach dealing with the lack of degradation data in the offline phase. First, features are computed on the collected raw signal, then One Class Support Vector Machine (OCSVM) is used to detect the degradation, this anomaly detection method is trained using only normal operation data. Then, features are ranked according to the selection criteria. The feature having the highest score is chosen as Health Indicator (HI). Finally an adaptive degradation model is applied for the prediction of the degradation evolution over time and Remaining Useful Life (RUL) estimation. The proposed approach is validated using run-to-failure vibration data collected from a high speed shaft bearings of a commercial wind turbine.
Hybrid Qualitative and Quantitative Diagnosis Approach
Authors: Matei Ion , Zhenirovskyy Maksym , De Kleer Johan , Feldman Alexander
Published: February 2021

Parametric faults are detected and isolated using parameter tracking algorithms based on optimization algorithms or filtering techniques (e.g., Kalman filter, particle filter). Online, simultaneous tracking of all parametric faults can fails since there may be too many combinations of parameter values that explain the observed behavior. Hence, a correct diagnosis solution is not obtained. An alternative in the single fault case is to track separately each parametric fault in parallel and choose the one that best explains the observed behavior according to some chosen metric (e.g., mean square error). This approach is feasible but computationally expensive, since there may be too many tracking algorithms running in parallel. We propose using analytic redundancy relations (ARRs) to reduce the number of parametric faults that are tracked simultaneously. ARRs qualitatively point to a set of possible explanations but usually require a large number of sensors to achieve good isolability of fault causes. They induce a fault signature matrix (FSM) that can be derived offline. The parameter tracking algorithms will be instantiated for the faults in the set of possible explanations produced by the ARRs. By combining ARRs with online parameter tracking algorithms we can obtain a good tradeoff between computational effort and fault isolability. We demonstrate our approach by diagnosing faults in a rectifier circuit.
Advanced Fault-tolerant Control Strategy of Wind Turbine Based on Squirrel Cage Induction Generator with Rotor Bar Defects
Authors: Roubah Boubakeur , Toubakh Houari , Sayed-Mouchaweh Moamar
Published: February 2021

Several research works in Wind Energy Conversion Systems (WECS) based on Squirrel Cage Induction Generator (SCIG) are developed for isolated site applications. Where, WECS are usually controlled by the Maximum Power Point Trucking (MPPT) algorithm in order to extract the maximum power from wind with constant operating point of SCIG. However, this WECS based on SCIG generator suffers from the problem of breaking rotor bars, therefore a failure in rotor bars is particularly a challenging task because:  1) it can change the operating point generated by MPPT algorithm, 2) increase mechanical vibrations, 3) affect the operation of gearbox, 4) inject a harmonic current in the grid. Therefore, Fault Tolerant Control (FTC) scheme is necessary to allow an optimum energy production, to reduce the maintenance costs and to increase the WECS availability. In this work, a new fault diagnosis approach based on frequency analysis of stator currents is used to detect the failure in bars of SCIG, then FTC algorithm is developed to adjust the pitch angle in max wind speed zone by determining a new operating point according to the wind speed, which leads to extend the lifetime of WECS. Simulation results using MATLAB SIMULINK showed a nominal current in healthy rotor bars and the lifetime of WECS was increased.
Hybrid PSO-SVM Based Method for Degradation Process Prediction of Reciprocating Seal
Authors: Ramachandran Madhumitha , Keegan Jon , Siddique Zahed
Published: February 2021

Reciprocating machines such as piston pumps, compressors and internal combustion engines are widely used in several manufacturing industries including automobile, aircraft, paper, oil and gas, etc. Reciprocating seal located directly on the rod/piston of a reciprocating equipment is used for preventing leakage and reducing wear between two parts that are in relative motion. Seals failure is one of the foremost causes of breakdown of reciprocating machinery and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessment of reciprocating seal is extremely important in the manufacturing industry to avoid fatal breakdown of reciprocating equipment and machines. Prediction of time series using predictive maintenance practices and tools to estimate the evolution of the future conditions of the system is of great interest to the operators for taking timely and appropriate maintenance decisions.  In this paper, we have built and trained a hybrid PSO-SVM model to predict the reciprocating seal degradation. Particle swarm optimization is used to optimize the penalty factor and kernel parameter of SVM model. Controlled experiments are designed and performed, and data collected from a dedicated experimental set-up is used to validate the proposed approach.
Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks
Authors: Basak Sanchita , Ayman Afiya , Laszka Aron , Dubey Abhishek , Leao Bruno
Published: February 2021

Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. We are specifically interested in analyzing the cascade effects of traffic congestions caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial  information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a microscopic traffic simulator. Our results show that we are able to build LSTM-based traffic-speed predictors with an average loss of 6.55 × 10^−4 compared to Gaussian Process Regression based predictors with an average loss of 1.78 × 10^−2. We are also able to detect anomalies with high precision and recall, resulting in an AUC of 0.8507 for the precision-recall curve. Finally, formulating the cascade propagation problem as a Timed Failure Propagation Graph, we are able to identify the source of a failure accurately.
Fatigue Crack Length Estimation and Prediction using Trans-fitting with Support Vector Regression
Authors: Youn Myeongbaek , Kim Yunhan , Lee Dongki , Cho Minki
Published: February 2021

This paper explains the proposed method of Team SeouLG in detail for crack length estimation and prediction based on wave signals. The proposed method consists of two parts; (1) crack length estimation using support vector regression (SVR), (2) crack length prediction using new trans-fitting method. For the estimation of the crack length, we define the features based on the filtered wave signals and construct the model using SVR method. The hyper-parameters of the SVR model are selected based on grid search algorithm. The prediction of the crack length is based on the previous crack length, which is estimated based on the wave signals. In this part, we apply a new proposed trans-fitting method. The trans-fitting method updates the selected candidate function to fit the trend of the crack propagation from the training dataset. By translocating the selected candidate functions to the estimated crack length, we can predict the crack length of the target cycles. The proposed method is validated with the new given specimens. The results show that the proposed method can estimate and predict the crack length to lead Team SeouLG to the first place in 2019 PHM Conference Data Challenge.
Physics Based Deep Learning Technique for Prognostics
Authors: Akkad Khaled
Published: February 2021

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.
Fault Diagnostics and Prognostics for Vehicle Springs and Stablizer Bar
Authors: Du Xinyu , Mai Lichao , Sadjadi Hossein
Published: February 2021

Vehicle springs and stabilizer bar are critical suspension components impacting vehicle riding and handling experience. Diagnostics and prognostics of springs and stabilizer bar can improve customer perceived quality, reduce repair cost and increase up-time for fleet vehicles. It’s even more important for autonomous vehicles, since there is no human driver to sense fault symptoms. Currently, there is no production solution to automatically diagnose and prognose spring and stabilizer bar failures, and most research work is suffered by various noise factors. In this work, a novel solution based on static ramp test is proposed to isolate and localize spring and stabilizer bar faults. With limited number of longitudinal and lateral acceleration measurements, the solution can quickly and effectively isolate faulty spring, disconnected stabilizer bar, loose bushing and loose end link. The validation results from a MY17 Bolt EV demonstrate the effectiveness and robustness of the proposed solution.
Large-scale Vibration Monitoring of Aircraft Engines from Operational Data using Self-organized Models
Authors: Forest Florent , Cochard Quentin , Noyer Cecile , Joncour Marc , Lacaille Jérôme , Lebbah Mustapha , Azzag Hanene
Published: February 2021

Vibration analysis is an important component of industrial equipment health monitoring. Aircraft engines in particular are complex rotating machines where vibrations, mainly caused by unbalance, misalignment, or damaged bearings, put engine parts under dynamic structural stress. Thus, monitoring the vibratory behavior of engines is essential to detect anomalies and trends, avoid faults and improve availability. Intrinsic properties of parts can be described by the evolution of vibration as function of rotation speed, called a vibration signature. This work presents a methodology for large-scale vibration monitoring on operating civil aircraft engines, based on unsupervised learning algorithms and a flight recorder database. Firstly, we present a pipeline for massive extraction of vibration signatures from raw flight data, consisting in time-domain medium-frequency sensor measurements. Then, signatures are classified and visualized using interpretable self-organized clustering algorithms, yielding a visual cartography of vibration profiles. Domain experts can then extract various insights from resulting models. An abnormal temporal evolution of a signature gives early warning before failure of an engine. In a post-finding situation after an event has occurred, similar at-risk engines are detectable. The approach is global, end-to-end and scalable, which is yet uncommon in our industry, and has been tested on real flight data.
A Hybrid Learning Approach to Prognostics and Health Management Applied to Military Ground Vehicles Using Time-Series and Maintenance Event Data
Authors: Bond W Glenn , Dozier Haley , Arnold Thomas L , Lam Michael Y , Dong Quyen T , Shukla Indu , Hansen Brandon E , Silas Antoinette R , Prieto Jeremiah , Mize Corey Hollis
Published: February 2021

Attempts to leverage operational time-series data in Condition Based Maintenance (CBM) approaches to optimize the life cycle management and Reliability, Availability, and Maintainability (RAM) of military vehicles have encountered several obstacles over decades of data collection. These obstacles have beset similar approaches on civilian ground vehicles, as well as on aircraft and other complex systems. Analysis of operational data is critical because it represents a continuous recording of the state of the system. Applying rudimentary data analytics to operational data can provide insights like fuel usage patterns or observed reliability of one vehicle or even a fleet. Monitoring trends and analyzing patterns in this data over time, however, can provide insight into the health of a vehicle, a complex system, or a fleet, predicting mean time to failure or compiling logistic or life cycle needs. Such High-Performance Data Analytics (HPDA) on operational time-series datasets has been historically difficult due to the large amount of data gathered from vehicle sensors, the lack of association between clusters observed in the data and failures or unscheduled maintenance events, and the deficiency of unsupervised learning techniques for time-series data. We present an HPDA environment and a method of discovering patterns in vehicle operational data that determines models for predicting the likelihood of imminent failure, referred to as Parameter-Based Indicators (PBIs). Our method is a data-driven approach that uses both time-series and relational maintenance data. This hybrid approach combines both supervised and unsupervised machine learning and data analytic techniques to correlate labeled, relational maintenance event data with unlabeled operational time-series data utilizing the DoD High Performance Computing (HPC) capabilities at the U.S. Army Engineer Research and Development Center. In leveraging both time-series and relational data, we demonstrate a means of fast, purely data-driven model creation that is more broadly applicable and requires less a priori information than physics informed, data-driven models. By blending these approaches, this system will be able to relate some lifecycle management goals through the workflow to generate specific PBIs that will predict failures or highlight appropriate areas of concern in individual or collective vehicle histories.
Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines
Authors: Jiang Xiaomo , Wang Fumin , Zhao Haixin , Xu Shengli , Lin Lin
Published: February 2021

Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.
Hierarchical structural health monitoring of wind turbines based on an overview of mechanical damage
Authors: Takeda Norio , Kameyama Tatsuya
Published: February 2021

A metric of giving an overview of mechanical damage has been proposed to practically realize the structural health monitoring of mechanical system using IoT data. In the case that a mechanical system includes many local hot spots, such as welded and bolt joints, where structural health should be monitored, the health monitoring system for all spots could be a large and expensive one. To overcome this drawback and increase the number of machines under structural health monitoring, the concept of a hierarchical monitoring was applied to the structural health monitoring of wind turbines in this development. In the first stage of the hierarchical monitoring, an overview of mechanical damage is given using the proposed metric, and accordingly, target machines are extracted for additional monitoring. The local damage can be accurately evaluated at the hot spots in the additional monitoring. As the result of employing the metric of giving the overview for several wind turbines, the extracted turbines corresponded to the turbines that are operated under severe wind conditions and expected for the accumulation of mechanical damage. It can be therefore concluded that the concept of the hierarchical monitoring is effective to the expansion of the monitoring target.
Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM)
Authors: AlDulaimi Ali , Mohammadi Arash , Asif Amir
Published: February 2021

The parallel hybrid models of different deep neural networks architectures are the most promising approaches for remaining useful life (RUL) estimation. In light of that, this paper introduces for the first time in the literature a new parallel hybrid deep neural network (DNN) solution for RUL estimation, named as the Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM). The proposed framework comprises of three parallel paths, the first one utilizes a noisy Bidirectional Long-short term memory (BLSTM) that used for extracting temporal features and learning the dependencies of sequence data in two directions, forward and backward, which can benefit completely from the input data. While the second parallel path employs noisy multilayer perceptron (MLP) that consists of three layers to extract different class of features. The third parallel path utilizes noisy convolutional neural networks (CNN) to extract another class of features. The concatenated output of the previous parallel paths is then fed into a noisy fusion center (NFC) to predict the RLU. The NMPM has been trained based on a noisy training to enhance the generalization behavior, as well as strengthen the model accuracy and robustness. The NMPM framework is tested and evaluated by using CMAPSS dataset provided by NASA.
Data-driven Residual Generation for Early Fault Detection with Limited Data
Authors: Khorasgani Hamed , Farahat Ahmed , Gupta Chetan
Published: February 2021

Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial applications for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to update and retrain the diagnosers as the system or the environment change over time automatically. Finally, unlike the model-based methods it is straightforward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model- based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We compare the performance of our proposed approach with traditional model-based methods through a case study.
Unsupervised Learning based Degradation Estimation and Abnormal Working Condition Detection for a Class of Anthropomorphic Robotic Hand
Authors: Cortés Mac-Evoy Carlos F. , Manrique Garay Jonathan , Kirchner Frank
Published: February 2021

Grasping is one of the most common tasks related to robotics and manipulation which has received an extensive amount of contributions from the research community. From a design point of view, the robotic gripper systems are generally manufactured using a significant amount of small moving parts, in order to establish a balance between size, weight and performance. This balance leads to designs and components that are less robust than those of, for example, pneumatic grippers. To the best of our knowledge, most of the literature related to robotic grasping concentrates and focuses on grasping from a cognitive perspective. However, in order to ensure the execution of grasping tasks over extended periods of time, reducing down times and increasing gripper availability, even in demanding scenarios without access to maintenance, other phenomena such as component tear and degradation have to be monitored and analysed. This paper proposes an unsupervised learning model based approach for the estimation of the degradation states and the detection of abnormal working conditions of the actuator components for a class of robotic anthropomorphic hand. The approach allows an easy implementation and establishes the basis for the development of remaining useful life estimation algorithms for the components of other gripper systems. Our proposed architecture consists of an automatic degradation estimator and working condition detector, based on an unsupervised model combining K-means and Gaussian Mixture Models. The model estimates the hand's actuators degradation and determines its working condition from the online data collected during grasping tasks considering different objects. The proposed method was experimentally tested on a real Schunk SVH Hand used to assist humans during the assembly process in the automobile industry.
A Study of Convolutional Neural Networks Learning Mechanisms for Machine Health Monitoring Applications
Authors: Alabsi Mohammed , Mohammad Ahmad Saeed , Sammoud Ahmed
Published: February 2021

In recent years, Deep Learning (DL) and Internet of Things (IoT) technologies have been used and deployed jointly to solve a wide range of modern technical challenges in different areas. With the continuous advancement of IoT connectivity solutions, the range of applications that can benefit from such an increase is limitless. One area that can benefit significantly from the combined strength of DL and IoT technologies is Machine Health Monitoring (MHM) Systems. MHM utilizes different analytical approaches and tools to determine the state and health of different components in running machinery. The traditional MHM system uses control limits from predetermining values that determine if a component has failed depending on the preset limits of the machinery. The main disadvantage of using such technique us the unpredictable nature of the timing and component failure. This type of failure causes unplanned production time loss and increases the cost of maintenance due to the unpredictability of the failure events. With DL and low-cost sensors that use different IoT connectivity solutions, MHM systems can utilize the learning capabilities of the DL network to perform end-to-end prognosis. One crucial fact is that features learned by Deep Neural Networks (DNN) are part of a large black box, and there are valuable underlying physical meanings embedded within the features. Hence, there is an exciting research area to explore underlying mechanisms and interpret physical meanings within DNN. In this paper, DNN learning mechanisms are evaluated using three different models: stacked autoencoders (SAE), denoising autoencoders (DAE), and convolutional neural networks (CNN). Initial results indicate that the input layer behaves similarly to a band-pass filter.  However, deep layers require optimal input design to maximize neuron activation, which leads to an extensive understanding of deep layer learning consequently (In progress).
Contending Remaining Useful Life Algorithms
Authors: Bechhoefer Eric , Dube Marc
Published: February 2021

Operational readiness, reliability and safety are all enhanced through condition monitoring. That said, for many assets, there is still a need for a prognostic capability to calculate remaining useful life (RUL). RUL allows operation and maintenance personnel to better schedule assets, and logisticians to order long lead time part to help improve balance of plant/asset availability. While a number of RUL techniques have been reported, we have focused on fatigue crack growth models (as opposed to physics or deep learning of based models). This paper compares the performance of stress intensity models (linear elastic model, e.g. Paris’ Law), to Head’s theory (geomatical similarity hypothesis) and to Dislocation/Energy theories of crack growth. It will be shown that these models differ mainly in the crack growth exponent, and that this leads to large differences in the estimation of RUL during early state fault propagation, though the results of all three models converge as the RUL is shorted.