bluuebunny/crossref_metadata_2025 huggingface dataset:
Prognostic-oriented Fuel Cell Catalyst Aging Modeling and Its Application to Health-Monitoring and Prognostics of a PEM Fuel Cell
Authors: Zhang Xian , Pisu Pierluigi
Published: March 2021

Today, poor long-term performance and durability combined with high production and maintenance costs remain the main obstacles for the commercialization of the polymer electrolyte membrane (PEM) fuel cells (PEMFCs). While on-line diagnosis and operating condition optimization play an important role in addressing the durability issue of the fuel cell, health-monitoring and prognosis (or PHM) techniques are of equally great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems.The two essential components of a PHM scheme for a general engineering system are 1) an accurate aging model that is capable of capturing the system’s gradual health deterioration, and 2) an algorithm for damage estimation and prognostics. In this paper, a physics-based, prognostic-oriented fuel cell catalyst degradation model is developed to characterize the relationship between the operating conditions and the degradation rate of the electro-chemical surface area (ECSA). The model complexity is kept minimal for on-line prognostic purpose. An unscented Kalman filter (UKF) approach is then proposed for the purpose of damage tracking and remaining useful life prediction of a PEMFC.
Adaptive Supervision of Patterns in Discrete Event Systems: Application to Crisis Management
Authors: Traore M. , Sayed-Mouchaweh M. , Billaudel P.
Published: March 2021

Crisis management is currently an important challenge for medical service and research. This motivates the development of new decision system approaches to assist (or to guide) the decision makers. A crisis management is a special type of collaboration involving several actors. The context and characteristics of crisis such as extent of actors and their roles make the crisis management more difficult in order to take decision. In this paper, we propose to model the interaction between different actors involved in crisis management. For this purpose we use finite state automaton in order to optimize the emergency response to the crisis and to reduce the disastrous consequences on people and environment. Thus, an adaptive supervision method is proposed. Therefore, we address the problem of diagnosis and prediction (prognostic) given an incomplete model of the discrete event systems of a crisis situation. When the model is incomplete, we introduce learning into the diagnoser (diagnosis module) construction.
Risk Prediction of Engineering Assets: An Ensemble of Part Lifespan Calculation and Usage Classification Methods
Authors: Kim Hyunjae , Hwang Taewan , Park Jungho , Oh Hyunseok , Youn Byeng D.
Published: March 2021

For the 2014 Prognostics and Health Management (PHM) Data Challenge Competition, the PHM Society proposed a problem surrounding risk prediction of engineering assets. We worked to address this problem by statistically analyzing the maintenance records, extracting key data features, and proposing an ensemble method for accurate prediction of imminent failure of assets. The data analysis of maintenance records provided two key pieces of information: 1) parts and part replacement reasons were able to be classified into corrective and scheduled maintenance actions, and 2) a linear relation was found between failure frequency and usage time. Based on this information, we proposed two risk-prediction methods, namely, a method based on part lifespan calculation and a method based on usage classification. Further work showed that the ensemble approach, which combined these two methods with a risk assignment formulation, provided more accurate risk prediction. The score predicted by the ensemble approach ranked in the second place in the 2014 PHM Data Challenge Competition.
Envelope Analysis on Vibration Signals for Stator Winding Fault Early Detection in 3-Phase Induction Motors
Authors: Jin Chao , Ompusunggu Agusmian P. , Liu Zongchang , Ardakani Hossein D. , Petré Fredrik , Lee Jay
Published: March 2021

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor condition-based maintenance methods usually involve current and voltage measurements, which could be expensive to collect, and vibration-based analysis is often only capable of detecting the fault when it is already quite significant. In contrast, the solution presented in this study provides a refreshing perspective by applying time synchronous averaging to remove the discrete frequency component, and amplitude demodulation to further enhance the signal with the help of kurtogram. Experimental results on a three-phase induction motor show that the method is also able to distinguish different fault severity levels.
Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI)
Authors: Abu-Samah Asma , Kashif Shahzad Muhammad , Zama¨ı Eric , Hubac St´ephane
Published: March 2021

Increasing demand diversity and volume in semiconductor industry (SI) have resulted in shorter product life cycles. This competitive environment, with high-mix low-volume production,requires sustainable production capacities that can be achieved by reducing unscheduled equipment breakdowns. The fault detection and classification (FDC) is a well-known approach, used in the SI, to improve and stabilize the production capacities. This approach models equipment as a single unit and uses sensors data to identify equipment failures against product and process drifts. Besides its successful deployment for years, recent increase in unscheduled equipment breakdown needs an improved methodology to ensure sustainable capacities. The analysis on equipment utilization, using data collected from a world reputed semiconductor manufacturer, shows that failure durations as well as number of repair actions in each failure have significantly increased. This is an evidence of misdiagnosis in the identification of failures and prediction of its likely causes. In this paper, we propose two lines of defense against unstable and reducing production capacities. First, equipment should be stopped only if it is suspected as a source for product and process drifts whereas second defense line focuses on more accurate identification of failures and detection of associated causes. The objective is to facilitate maintenance engineers for more accurate decisions about failures and repair actions, upon an equipment stoppage. In the proposed methodology, these two lines of defense are modeled as Bayesian network (BN) with unsupervised learning of structure using data collected from the variables (classified as symptoms) across production, process, equipment and maintenance databases. The proofs of concept demonstrate that contextual or statistical information other than FDC sensor signals, used as symptoms, provide reliable information (posterior probabilities) to find the source of product/process quality drifts, a.k.a. failure modes (FM), as well as potential failure and causes. The reliability and learning curves concludes that modeling equipment at module level than equipment offers 45% more accurate diagnosis. The said approach contributes in reducing not only the failure durations but also the number of repair actions that has resulted in recent increase in unstable production capacities and unscheduled equipment breakdowns.
Fault Diagnosis in Fuzzy Discrete Event System: IncompleteModels and Learning
Authors: Traore M. , Chˆatelet Eric , Soulier Eddie , Gabbar Hossam A.
Published: March 2021

Nowadays, determining faults in non-stationary environment and that can deal with the problems of fuzziness impreciseness and subjectivity is a challenging task in complex systems such as nuclear center, or wind turbines, etc. Our objective in this paper is to develop models based on fuzzy finite state automaton with fuzzy variables describing the industrial process in order to detect anomalies in real time and possibly in anticipation. A diagnosis method has for goal to alert actors responsible for managing operations and resources, able to adapt to the emergence of new procedures or improvisation in the case of unexpected situations. The diagnoser module use the outputs events and membership values of each active state of the model as input events.
Diagnosis of an Alternator System Using Quantized Approaches
Authors: Mohon Sara , Pisu Pierluigi
Published: March 2021

In this paper, the Generalized Cell Mapping (GCM) method for a linear system is compared with a new stochastic method for novel cell-to-cell mapping. The authors presented the new stochastic method in Mohon and Pisu (2013). The two methods are compared in an application example of a vehicle alternator. The alternator may experience three faults including belt slippage, a faulty diode connection, or incorrect controller reference voltage. Fault detection and isolation (FDI) is performed using the two cell-to-cell mapping methods. The results show that the new stochastic method is slower but yields better isolation results than the GCM method.
A Review of Prognostics and Health Management Applications in Nuclear Power Plants
Authors: Coble Jamie , Ramuhalli Pradeep , Bond Leonard , Hines J. Wesley , Upadhyaya Belle
Published: March 2021

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40- year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation. Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many small modular reactor (SMR) and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities.
Micro Displacement and Strain Detection for Crack Prediction on Concrete Surface Using Optical Nondestructive Evaluation Methods
Authors: Luo Longxi , Feng Maria , Fukuda Yoshio , Zhang Chao
Published: March 2021

Continuous inspection of the concrete structures is important for extending the operating life span of nuclear power plants. Restricted physical accessibility to the nuclear plant structures, due to concerns of radiation, presents a unique challenge to the conventional visual inspection and contact-type nondestructive evaluation (NDE) technologies. Digital image correlation (DIC) is an optical NDE method that can measure the structural parameters such as displacement and strain. However, it is highly challenging to accurately detect micro displacement on the concrete surface because of weathering and change in illumination conditions. Usually, an artificial speckle pattern with good contrast to the object surface is needed for calibration and tracking, but it is difficult to apply in the field. In order to be able to detect micro surface strain for crack prediction in outdoor environment, a DIC-based NDE technology is developed to significantly improve the measurement accuracy by incorporating the orientation code matching (OCM) technique, a robust and accurate template matching algorithm. Concrete specimens were built and tested under four-point bending. Using the remotely measured images, the OCM incorporated DIC method successfully predicted concrete cracks before they emerged on the surface. The experiments also demonstrated the robustness of the method against the optical noise including weathering and change in illumination conditions.
Improved Probabilistic Modeling of Multi-Site Fatigue Cracking
Authors: Al Tamimi Abdallah , Modarres Mohammad
Published: March 2021

The purpose of this paper is to investigate the effect of fatigue, in the presence of neighboring cracks, and to integrate that into a model that could be used to predict crack growth. A total of 20 fatigue experiments were performed at different loading conditions using dog-bone samples of API-5L grade B carbon steel containing neighboring cracks. The impact of the neighboring cracks dimensions and the loading conditions on the interaction, coalescence and growth of cracks were investigated. A design of experiment approach to study neighboring cracks interactions and growth in carbon steel materials is also presented. Simulation efforts were performed to assess the Stress Intensity Factor (SIF) around neighboring cracks. Models discussing how the SIF of single semi-elliptical crack could be corrected to account for the neighboring cracks interaction were discussed in order to better understand the fatigue behavior. A combination of these models was integrated to find the SIF values necessary for the probabilistic life prediction modeling purposes. Finally, a multi-site fatigue crack growth rate model was developed and its parameters including their uncertainties were estimated. A Bayesian approach was adopted to perform uncertainty characterization and model validation.
International Journal of Prognostics and Health Management, ISSN 2153-2648, 2015 007 Effectiveness of vibration monitoring in the health assessment of induction motor
Authors: Kumar T Ch Anil , Singh Gurmeet , Naikan V N A
Published: March 2021

Induction motors are widely used prime movers for rotating machinery in most of the industrial applications. Reliability of the induction motors plays a significant role in reduction in downtime of the machinery. Proper diagnostic procedures are to be followed to assess the condition of the motor. Based on the literature, it is found that the vibration analysis is mostly used for diagnosis of rotating systems. However, industrial experiences reveal that potential motor failures have been observed very frequently even with proper vibration based condition monitoring. Therefore, this paper focuses on further investigations of whether the vibration monitoring stand alone can properly diagnose the presence of faults such as eccentricity in an induction motor or any other alternative technique should be employed in addition to the vibration monitoring for better diagnosis. In this paper, experimental investigations have been carried out to monitor the changes in the vibration and current spectrum of an eccentric motor in its decoupled and coupled state with a healthy rotor system.
Impact of Prognostic Uncertainty in System Health Monitoring
Authors: Vandawaker Robert M. , R. Jacques David , K. Freels Jason
Published: March 2021

Across many industries, systems are exceeding their intended design lives, whether they are ships, bridges or military aircraft. As a result failure rates can increase and unanticipated wear or failure conditions can arise. Health monitoring research and application has the potential to more safely lengthen the service life of a range of systems through utilization of sensor data and knowledge of failure mechanisms to predict component life remaining. A further benefit of health monitoring when combined across an entire platform is system health management. System health management is an enabler of condition based maintenance, which allows repair or replacement based on material condition, not a set time. Replacement of components based on condition can enable cost savings through fewer parts being used and the associated maintenance costs. The goal of this research is to show the management of system health can provide savings in maintenance and logistics cost while increasing vehicle availability through the approach of condition based maintenance.This work examines the impact of prediction accuracy uncertainty in remaining useful life prognostics for a squadron of 12 aircraft. The uncertainty in this research is introduced in the system through an uncertainty factor applied to the useful life prediction. An ARENA discrete event simulation is utilized to explore the effect of prediction error on availability, reliability, and maintenance and logistics processes. Aircraft are processed through preflight, flight, and post-flight operations, as well as maintenance and logistics activities. A baseline case with traditional time driven maintenance is performed for comparison to the condition based maintenance approach of this research.This research does not consider cost or decision making processes, instead focusing on utilization parameters of both aircraft and manpower. The occurrence and impact of false alarms on system performance is examined. The results show the potential availability, reliability, and maintenance benefits of a health monitoring system and explore the diagnostic uncertainty.
An Inference-based Prognostic Framework for Health Management of Automotive Systems
Authors: Sankavaram Chaitanya , Kodali Anuradha , Pattipati Krishna , Singh Satnam , Zhang Yilu , Salman Mutasim
Published: March 2021

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.
Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals
Authors: Saimurugan M. , Nithesh R.
Published: March 2021

The failure of rotating machine elements causes unnecessary downtime of the machine. Fault in the rotating machinery can be identified from noises, vibration signals obtained from sensors. Bearing and shaft are the most important basic rotating machine elements. Detection of fault from vibration signals is widely used method in condition monitoring techniques for diagnosis of machine elements. Fault diagnosis from sound signals is cost effective than vibration signals. Sound signal analysis is not well explored in the field of automated fault diagnosis. Under various simulated fault conditions, the sound signals are obtained by placing microphone near the bearing for different speeds. The features are extracted by using statistical and histogram methods. The best features of sound signals are obtained by decision tree algorithm. The extracted features are used as inputs to the classifier-Artificial Neural Network. The classification accuracy results from statistical and histogram features are obtained and compared.
Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing
Authors: Jin Xiaoning , A. Weiss Brian , Siegel David , Lee Jay
Published: March 2021

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.
Temporal Learning in Video Data Using Deep Learning and Gaussian Processes
Authors: K. Jha Devesh , Srivastav Abhishek , Ray Asok
Published: March 2021

This paper presents an approach for data-driven modeling of hidden, stationary temporal dynamics in sequential images or videos using deep learning and Bayesian non-parametric techniques. In particular, a deep Convolutional Neural Network (CNN) is used to extract spatial features in an unsupervised fashion from individual images and then, a Gaussian process is used to model the temporal dynamics of the spatial features extracted by the deep CNN. By decomposing the spatial and temporal components and utilizing the strengths of deep learning and Gaussian processes for the respective sub-problems, we are able to construct a model that is able to capture complex spatio-temporal phenomena while using relatively small number of free parameters. The proposed approach is tested on high-speed grey-scale video data obtained of combustion flames in a swirl-stabilized combustor, where certain protocols are used to induce instability in combustion process. The proposed approach is then used to detect and predict the transition of the combustion process from stable to unstable regime. It is demonstrated that the proposed approach is able to detect unstable flame conditions using very few frames from high-speed video. This is useful as early detection of unstable combustion can lead to better control strategies to mitigate instability. Results from the proposed approach are compared and contrasted with several baselines and recent work in this area. The performance of the proposed approach is found to be significantly better in terms of detection accuracy, model complexity and lead-time to detection.
A Bayesian Approach to Fault Identification in the Presence of Multi-component Degradation
Authors: Lin Yufei , Zakwan Skaf , Jennions Ian
Published: March 2021

Fault diagnosis typically consists of fault detection, isolation and identification. Fault detection and isolation determine the presence of a fault in a system and the location of the fault. Fault identification then aims at determining the severity level of the fault. In a practical sense, a fault is a conditional interruption of the system ability to achieve a required function under specified operating condition; degradation is the deviation of one or more characteristic parameters of the component from acceptable conditions and is often a main cause for fault generation. A fault occurs when the degradation exceeds an allowable threshold. From the point a new aircraft takes off for the first time all of its components start to degrade, and yet in almost all studies it is presumed that we can identify a single fault in isolation, i.e. without considering multi-component degradation in the system. This paper proposes a probabilistic framework to identify a single fault in an aircraft fuel system with consideration of multi-component degradation. Based on the conditional probabilities of sensor readings for a specific fault, a Bayesian method is presented to integrate distributed sensory information and calculate the likelihood of all possible fault severity levels. The proposed framework is implemented on an experimental aircraft fuel rig which illustrates the applicability of the proposed method.
An Integrated Architecture for Corrosion Monitoring and Testing, Data mining, Modeling and Diagnostics/Prognostics
Authors: Li Honglei , Garvan Margaret R , Li Jiaming , Echauz Javier , J. Vachtsevanos George , W. Brown Douglas , J. Connolly Richard , Zahiri Frank
Published: March 2021

It has been established that corrosion is one of the most important factors causing deterioration and decreased performance and reliability in critical aerospace and industrial systems. Corrosion monitoring, detection, and quantification are recognized as key enabling technologies to reduce the impact of corrosion on the integrity of aircraft and industrial assets. Accurate and reliable detection of corrosion initiation and propagation, with specified false alarm rates, requires novel tools and methods, including verifiable simulation and modeling methods. This paper reports an experimental investigation of the detection and quantification of pitting corrosion on aluminum alloy panels using 3D surface metrology methods and image processing techniques. Panel surfaces were evaluated by laser microscopy and stylus-based profilometry to characterize global and local surface features. Promising imaging and texture features were extracted and compared between coated and uncoated aluminum panels at different exposure times under accelerated corrosion conditions. Image processing, information processing, and data mining techniques were utilized to evaluate the presence and extent of pitting corrosion. A new modeling framework for corrosion stages is introduced that emphasizes the representation of pitting corrosion and ultimately the crack formation process. Detection and prediction of the evolution of corrosion stages relies on data, a particle filtering method, and the corrosion propagation model. Results from these experimental studies demonstrate the efficacy of this proposed methodology.
Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests
Authors: B. Gurung Ram , Lindgren Tony , Bostr¨om Henrik
Published: March 2021

Being able to accurately predict the impending failures of truck components is often associated with significant amount of cost savings, customer satisfaction and flexibility in maintenanceservice plans. However, because of the diversity in the way trucks typically are configured and their usage under different conditions, the creation of accurate prediction models is not an easy task. This paper describes an effort in creating such a prediction model for the NOx sensor, i.e., a component measuring the emitted level of nitrogen oxide in the exhaust of the engine. This component was chosen because it is vital for the truck to function properly, while at the same time being very fragile and costly to repair. As input to the model, technical specifications of trucks and their operational data are used. The process of collecting the data and making it ready for training the model via a slightly modified Random Forest learning algorithm is described along with various challenges encountered during this process. The operational data consists of features represented as histograms, posing an additional challenge for the data analysis task. In the study, a modified version of the random forest algorithm is employed, which exploits the fact that the individual bins in the histograms are related, in contrast to the standard approach that would consider the bins as independent features. Experiments are conducted using the updated random forest algorithm, and they clearly show that the modified version is indeed beneficial when compared to the standard random forest algorithm. The performance of the resulting prediction model for the NOx sensor is promising and may be adopted for the benefit of operators of heavy trucks.
Non-Contact Quantification of Longitudinal and Circumferential Defects in Pipes using the Surface Response to Excitation (SuRE) Method
Authors: Baghalian Amin , Tashakori Shervin , Y. Senyurek Volkan , McDaniel Dwayne , Fekrmandi Hadi , N. Tansel Ibrahim
Published: March 2021

Rapid screening and monitoring of hollow cylindrical structures using active guided-waves based structural health monitoring (SHM) techniques are important in chemical, petro-chemical, oil and gas industries. Successful implementation of the majority of these techniques in the SHM of pipes depends on the identification of the appropriate guided-waves modes and their frequencies for each application. The highly dispersive nature of the guided-waves and presence of multi modes at each frequency makes the mode selection and the interpretation of signals a challenging task. The surface response to excitation (SuRE) method was developed to detect the defects and loading condition changes on plates with minimum dependence on the excitation of particular modes at certain frequencies. In the present study, the SuRE method is proposed for quantification of longitudinal and circumferential defects, with varying severities, as common examples of axisymmetric and nonaxisymmetric defects in pipes. The results indicate that the SuRE method can be used effectively for damage quantification in hollow cylinders.
Integrated Prognostics Observer for Condition Monitoring of an Automated Manual Transmission Dry Clutch System
Authors: Ramalingam Sivakumar , VV Prasad Hanumath , Prakash Regalla Srinivasa
Published: March 2021

The closed loop feedback control system of an Automated Manual Transmission (AMT) electro-pneumatic clutch actuator is used for intelligent real time condition monitoring, enhanced diagnostics and prognostic health management of the dry clutch system, by integrating with the existing gearbox prognostics observer. The real-time sensor data of the clutch actuator piston position is analyzed for monitoring the condition of the clutch system. Original parameters of the new clutch are stored in the Electrically Erasable Programmable Read-only Memory (EEPROM) of the AMT controller and the real-time data is used by the observer for assessing the degradation/wear of the frictional clutch parts. Also, clutch slip during torque transmission is monitored, using the engine speed and the gearbox input shaft speed from Controller Area Network (CAN). Condition monitoring of clutch system provides enhanced prognostic functionality for AMT system which ensures consistent clutch performance, gear shift quality and timely warning for recalibration, repair and/or replacement of the critical wear and tear parts. Also, systematic analysis of the monitored data provides an accurate diagnosis of a developing fault. Thus, with the advanced control systems in place for AMT, a closed loop feedback based condition monitoring system is modelled for improved diagnostics and prognostics of AMT clutch system.
Segmentation Based Feature Evaluation and Fusion for Prognostics
Authors: Atamuradov Vepa , Camci Fatih
Published: March 2021

Quantification of feature goodness, called feature evaluation, is crucial in the identification of best features and achieving high accuracy in diagnostics and prognostics. Even though feature evaluation for diagnostics is a mature area, it is a developing research area for prognostics. The feature goodness for prognostics is measured by change in degradation. Most, if not all, of existing methods, analyze the feature change in the whole failure degradation. In other words, features collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be a good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents a methodology that divides the features into segments, each of which may have different goodness for prognostics. Thus, some part of the feature may be good, whereas the others not. The presented approach leads to extract more value from the features’ changing properties during the failure degradation. The method has been applied to simulated and real datasets obtained from Li-ion batteries aging tests. State of health (SoH) estimation accuracy is enhanced with the presented approach.
Editorial Special Issue: PHM for Railway Systems and Mass Transportation
Authors: Andrews John , B¨ohm Thomas , Dersin Pierre , Groos J¨orn C. , Schenkendorf Ren´e
Published: March 2021

The railway and mass transportation system is composed of industrial goods with substantial capital investments and long life cycles. This applies to rolling stock like trains, locomotives,wagons, and even more to the infrastructure like signaling, catenary, tracks, bridges, and tunnels. The lifespan of rolling stock is 30 to 40 years while the infrastructure is used 30 to 60 years even more than 100 years in case of tunnels and bridges. As in other industrial goods, the cost drivers are determined in the early design phases but realized mainly during a long time of operation. Maintenance is one of the main cost drivers but essential to a reliable, capable, and – above all – safe operation.
On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
Authors: K¨allstr¨om Elisabeth , Olsson Tomas , Lindstr¨om John , Hakansson Lars , Larsson Jonas
Published: March 2021

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.
Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction
Authors: Yang Shuhua , Jiang Xiaomo , Xu Shengli , Wang Xiaofang
Published: March 2021

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor.
Scalable fault models for diagnosis in a synchronous generator using feature mapping and transformation techniques
Authors: Gopinath R. , Santhosh Kumar C. , Ramachandran R. I.
Published: March 2021

Condition based maintenance (CBM) needs data acquired during healthy and faulty conditions to develop intelligent system for fault diagnosis. However, fault injection is not allowed/possible in a highly expensive components of complex/critical systems to collect fault condition data. Therefore, proto-type/small working models are used to conduct experiments for abnormal/fault conditions, to obtain and scale the intelligence of the system for effective health monitoring of complex system. This methodology is referred as scalable fault models. For proof of concept, in this work, we considered two different capacity synchronous generators with rating of 3 kVA and 5 kVA to emulate the behavior of prototype/small working model and complex system respectively, for scalable fault models. We explored feature mapping and transformation techniques to achieve effective scalability.From the preliminary experiments, it is observed that the baseline system performance deteriorated due to the changes in the system (capacity) and its characteristics with load changes.We therefore, expressed the input features in terms of load and system independent manner, to make the features less dependent on load and system variations. We explored localityconstrained linear coding (LLC) to express the features load/system independently. It is observed that experimenting LLC with the backend support vector machine (SVM) classifier gave the best fault classification performance for linear kernel, suggesting that the faults are linearly separable in the new feature space.Since the LLC mapped feature space is linearly separable, we then explored linear feature transformation technique, nuisance attribute projection (NAP) on the LLC mapped feature space to further minimize the load/system specific variations. We observed that LLC-NAP improved the overall accuracy and sensitivity of the classifier significantly. We also noted that the performance of NAP was limited in the original feature space since the feature space (NAP without LLC) is nonlinear with load/system variations.
Development of a Real-Time Driver Health Detection System Using a Smart Steering Wheel
Authors: Lee Jae-Cheon , Liu Hao
Published: March 2021

The number of vehicle accidents due to driver drowsiness continues to increase. Therefore, prompt and effective detection for driver health during driving is crucial to improvement of traffic safety. A set of real-time health detection system built into a smart steering wheel for the driver is proposed in the paper. The driver's health condition (drowsiness) is detected by a developed algorithm by monitoring the driver’s biological signals, including respiration, hand grip force, photoplethysmogram (PPG), and electrocardiogram (ECG). Meanwhile the driver's state of arrhythmia, as a common cardiac disease, can be diagnosed too. The test results indicate that the developed real-time driver health detection system can effectively monitor the state of vigilance and the cardiac state, i.e. arrhythmia, of the driver.
Interactive Anomaly Identification with Erroneous Feedback
Authors: Tagawa Takaaki , Tadokoro Yukihiro , Yairi Takehisa
Published: March 2021

The difficulties in analyzing large and extensive systems necessitate the use of efficient machine-learning tools to identify unknown system anomalies in order to avoid critical problems and ensure high reliability. Given that data logged by a system include unknown anomalies, anomaly identification models aim to simultaneously identify the time of occurrence and the features that contributed to these anomalies. To maximize accuracy, it is important to utilize the data as well as the domain knowledge of the system. However, it is difficult for a system analyst to possess not only machine-learning capabilities but also domain knowledge to incorporate into the model. In this paper, we propose a new anomaly identification framework capable of utilizing feedback based on domain knowledge without requiring any machine-learning capabilities. We also propose a novel method, the so-called rank ensemble method, to improve the accuracy of anomaly identification with erroneous feedback, that is, feedback that in- cludes incorrect information. Our method enables erroneous information to be adaptively ignored by assuming consistency between the data and the user feedback. An intensive parameter study using benchmark datasets and a case study with real vehicle data demonstrate the applicability of our framework.
Comparative Effectiveness of a One-Year
Follow-Up of Thoracic Medial Branch Blocks
in Management of Chronic Thoracic Pain: A
Randomized, Double-Blind Active Controlled Trial
Authors: Manchikanti Laxmaiah
Published: March 2021

Background: Thoracic facet joints have been implicated as the source of chronic pain in the mid
back or upper back in 34% to 42% of patients when the modified criteria of the International
Association for the Study of Pain (IASP) is utilized. Various therapeutic techniques utilized in
managing chronic thoracic pain of facet joint origin include intraarticular injections, medial branch
blocks, and radiofrequency neurotomy of thoracic medial branch nerves.
Study Design: A randomized, double-blind, active controlled trial.
Setting: A private practice, interventional pain management setting, and a specialty referral
center setting in the United States.
Objective: To determine the clinical effectiveness of therapeutic local anesthetic medial branch
blocks with or without steroid in managing chronic function-limiting mid back or upper back pain
of facet joint origin.
Methods: The study was performed in an interventional pain management private practice, a
tertiary referral center, in the United States. A total of 100 participants were included, with 50
participants in each of the local anesthetic and steroid groups. All of the participants met the
diagnostic criteria of thoracic facet joint pain by means of comparative, controlled diagnostic
blocks and the inclusion criteria. Group I participants received thoracic medial branch blocks with
bupivacaine, whereas Group II participants received thoracic medial branch blocks with bupivacaine
and non-particulate betamethasone.
Outcomes Assessment: Outcomes measures included numeric rating scores (NRS), Oswestry
Disability Index (ODI), opioid intake, and return to work status at baseline, 3 months, 6 months,
and 12 months. Significant pain relief was defined as ≥ 50% pain relief and/or a positive change
in ODI scores.
Results: In Group I and Group II 90% of participants showed significant pain relief and functional
improvement at 12 months.
The majority of the participants experienced significant pain relief of 47.2 ± 10.1 weeks in Group I
and 46.3 ± 8.4 weeks in Group II, requiring approximately 3.5 treatments per year with an average
relief of 15.8 ± 10.5 in Group I and 13.6 ± 3.6 weeks in Group II per episode of treatment.
Limitations: Study limitations include the lack of a placebo group.
Conclusions: Therapeutic thoracic medial branch blocks, with or without steroid, may provide a
management option for chronic function-limiting mid back or upper back pain of facet joint origin.
Clinical Trial: NCT00355706
Key words: Chronic spinal pain, thoracic pain, thoracic facet or zygapophysial joint pain, facet
joint nerve or medial branch blocks, comparative controlled local anesthetic blocks, therapeutic
thoracic medial branch blocks.
Wireless Ice Detection and Monitoring using Flexible UHF RFID Tags
Authors: Wagih Mahmoud , Shi Junjie
Published: March 2021

Owing to its low relative permittivity, very few microwave sensors have been developed for monitoring ice deposition. This paper presents the first use of UHF RFID tags for wireless RF ice sensing applications. Despite its low permittivity, the existence of ice as a superstrate on a planar ultra-thin dipole antenna can lower the resonance frequency of the antenna significantly. The RFID tags, having a measured unloaded range of 9.4 m, were evaluated for remotely detecting the formation of ice in various scenarios and up to 10~m from the reader, as well as monitoring the ice thawing, based on the Relative Signal Strength (RSS) in a phase-free approach. Unlike conventional RSS-based sensing approaches where the tag's read-range is reduced as the RSS decreases in response to the stimulant, the ice superstrate improves the impedance matching of the tags, maintaining a 10 m loaded read-range with over 12 dB ice-sensitivity, in an echoic multi-path environment. The long range and high sensitivity show that UHF RFID is a promising method of detecting and monitoring ice formation and thawing in future smart cities.