bluuebunny/crossref_metadata_2025 huggingface dataset:
Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach
Authors: Jegadeeshwaran R. , Sugumaran V.
Published: March 2021

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned.
A Comparison of Edge Detectors in the Framework of Wake Pattern Modeling for Wind Turbines
Authors: Yan Yanjun , Zhang James Z. , Karayaka Hayrettin Bora
Published: March 2021

To monitor wind turbine health, wind farm operators can take advantage of the historical SCADA (supervisory control and data acquisition) data to generate the wake pattern beforehandfor each wind turbine, and then decide in real time whether observed reduction in power generation is due to wake or true faults. In our earlier efforts, we proposed an effective wakepattern modeling approach based on edge detector using Linear Prediction (LP) with entropy-thresholding, and smoothing using Empirical Mode Decomposition (EMD) on the windspeed difference plots. In this paper, we compare the LP based edge detector with two other predominant edge detectors, Sobel and Canny edge detectors, to quantitatively justifythe appropriateness and effectiveness of the LP based edge detector in wind turbine wake pattern analysis. We generate a fused wake model for the turbine of interest with multiple neighboring turbines, and then analyze the wake effect on turbine power generation. With a fused wake pattern, we do not need to identify the individual source of wake any more. Weexpect that wakes cause reduced wind speed and hence reduced power generation, but we have also observed from the SCADA data that the wind turbines in wake zones tend to overreact when the wind speed is not yet close to the highwind- shut-down threshold, which causes further power generation loss.
A Dynamic Weighted RBF-Based Ensemble for Prediction of Time Series Data from Nuclear Components
Authors: Liu Jie , Vitelli Valeria , Zio Enrico , Seraoui Redouane
Published: March 2021

In this paper, an ensemble approach is proposed for prediction of time series data based on a Support Vector Regression (SVR) algorithm with RBF loss function. We propose a strategy to build diverse sub-models of the ensemble based on the Feature Vector Selection (FVS) method of Baudat & Anouar (2003), which decreases the computational burden and keeps the generalization performance of the model. A simple but effective strategy is used to calculate the weights of each data point for different sub-models built with RBF-SVR. A real case study on a nuclear power production component is presented. Comparisons with results given by the best single SVR model and a fixed-weights ensemble prove the robustness and accuracy of the proposed ensemble approach.
Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors
Authors: Barbieri Federico , Hines J. Wesley , Sharp Michael , Venturini Mauro
Published: March 2021

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life.
Uncertainty in PHM
Authors: Sankararaman Shankar , Mahadevan Sankaran , Orchard Marcos E.
Published: March 2021

Uncertainty plays an important role in diagnostics, prognostics, and health management of engineering systems. The presence of uncertainty leads to an imprecise understanding of the behavior of such systems; as a result, this may adversely affect the results of diagnostics and prognostics. In particular, this may lead to an inaccurate estimation of the remaining useful life, which in turn affects operational decision-making. While several researchers have recognized the importance of uncertainty in prognostics and health management (PHM), there has not been a significant amount of research work that addresses the impact of uncertainty in different PHM activities. This is challenging because there are various sources of uncertainty that affect PHM, their interactions are not fully understood, and therefore, it is an arduous task to perform different PHM activities by systematically accounting for these sources of uncertainty. However, when this can be accomplished, it would be possible to estimate the uncertainty and confidence in the results of diagnostics and prognostics, and quantify the risk involved in prognostics-based decisionmaking.
Probabilistic Prognosis with Dynamic Bayesian Networks
Authors: Bartram Gregory , Mahadevan Sankaran
Published: March 2021

This paper proposes a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN). Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats from various sources and give a probabilistic representation of the system state. Further, DBNs provide a platform naturally suited for seamless integration of diagnosis, uncertainty quantification, and prediction. In the proposed methodology, a DBN is used for online diagnosis via particle filtering, providing a current estimate of the joint distribution over the system variables. The information available in the state estimate also helps to quantify the uncertainty in diagnosis. Next, based on this probabilistic state estimate, future states of the system are predicted using the DBN and sequential or recursive Monte Carlo sampling. Prediction in this manner provides the necessary information to estimate the distribution of remaining use life (RUL). The prognosis procedure, which is system specific, is validated using a suite of offline hierarchical metrics. The prognosis methodology is demonstrated on a hydraulic actuator subject to a progressive seal wear that results in internal leakage between the chambers of the actuator.
Hybrid Particle Petri Nets for Systems Health Monitoring under Uncertainty
Authors: Gaudel Quentin , Chanthery Elodie , Ribot Pauline
Published: March 2021

This paper focuses on how to treat uncertainty in health monitoring of hybrid systems by using a model-based method. The Hybrid Particle Petri Nets (HPPN) formalism is definedin the context of health monitoring to model hybrid systems and to generate diagnosers of such systems. The main advantage of this formalism is that it takes into account knowledgebased uncertainty and uncertainty in diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as faults) and is robust to false observations. It also estimates the continuous state of the system by using particle filtering. Finally, HPPN can represent the system degradation that is often dealt with using probabilistic tools. A hybrid technique is thus used to group all this knowledge and to deduce the diagnosis results. The approach is demonstrated on a three-tank system. Experimental results are given, illustrating how different kinds of uncertainty are taken into account when using HPPN.
International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008 PHM-Based Wind Turbine Maintenance Optimization Using Real Options
Authors: Lei Xin , A. Sandborn Peter
Published: March 2021

A simulation-based real options analysis (ROA) approach is used to determine the optimum predictive maintenance opportunity for a wind turbine with a remaining useful life (RUL) prediction. When an RUL is predicted for a subsystem in a single turbine using PHM, a predictive maintenance option is triggered that the decision-maker has the flexibility to decide if and when to exercise before the subsystem or turbine fails. The predictive maintenance value paths are simulated by considering the uncertainties in the RUL prediction and wind speed (that govern the turbine’s revenue earning potential). By valuating a series of European options expiring on all possible predictive maintenance opportunities, a series of option values can be obtained, and the optimum predictive maintenance opportunity can be determined. A case study is presented in which the ROA approach is applied to a single turbine.
A System-Based Approach to Monitoring the Performance of a Human Neuromusculoskeletal System
Authors: Mussleman Marcus , H. Gates Deanna , Djurdjanovic Dragan
Published: March 2021

This paper presents a system-based method for monitoring a human neuromusculoskeletal (NMS) system. It is based on autoregressive models with exogenous inputs, which link surface electromyographic signals and joint kinematic variables in order to detect changes in system dynamics, as well as to assess joint level and muscle level contributions to those changes. Instantaneous energy and mean frequency of time frequency distributions of electromyographic signals were used as model inputs, while angular velocities of the monitored joints served as outputs. Slow temporal changes in the behavior of the entire system or individual joint models were tracked by analyzing one-step ahead prediction errors of the corresponding models over time. Finally, analysis of the recursively updated models, which tracked the NMS dynamics over time, was used to characterize these changes at the joint and muscular levels. Themethodology is demonstrated on data recorded from 12 human subjects completing a repetitive sawing motion until voluntary exhaustion. Statistically significant decreasing trends in the similarities of the NMS models to those observed in the rested state were observed in all subjects. In addition, decreased joint response to muscle activity, as well as changes in the coordination and motion planning have been detected with all subjects, indicating their fatigue.
Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics
Authors: Qiao Guixiu , A. Weiss Brian
Published: March 2021

Unexpected equipment downtime is a ‘pain point’ for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.
A Review of Problem Structuring Methods for Consideration in Prognostics and Smart Manufacturing
Authors: T. Hester Patrick , J. Collins Andrew , Ezell Barry , Horst John
Published: March 2021

Successful use of prognostics involves the prediction of future system behaviors in an effort to maintain system availability and reduce the cost of maintenance and repairs. Recent work by the National Institute of Standards and Technology indicates that the field of prognostics and health management is vital for remaining competitive in today’s manufacturing environment. While prognostics-based maintenance involves many traditional operations researchcentric challenges for successful deployment such as limited availability of information and concerns regarding computational efficiency, the authors argue in this paper that the field of prognostics and health management, still in its embryonic development stage, could benefit greatly from considering soft operations research techniques as well. Specifically, the authors propose the use of qualitative problem structuring techniques that aid in problem understanding and scoping. This paper provides an overview of these soft methods and discusses and demonstrates how manufacturers might use them. An approach combining problem structuring methods with traditional operations research techniques would help accelerate the development of the prognostics field.
Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning
Authors: Sarkar Soumalya , Reddy Kishore , Dorgan Alex , Fidopiastis Cali , Giering Michael
Published: March 2021

It is of paramount importance to track the cognitive activity or cognitve attenion of the service personnel in a Prognostics and Health Monitoring (PHM) service related training or operation environment. The electroencephalography (EEG) data is one of the good candidates for cognitive activity recognition of the user. Analyzing electroencephalography (EEG) data in an unconstrained (natural) environment for understanding cognitive state and classifying human activity is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline availability and uncontrolled mixing of various tasks. This paper proposes a framework based on an emerging tool named deep learning that monitors human activity by fusing multiple EEG sensors and also selects a smaller sensor suite for a lean data collection system. Real-time classification of human activity from spatially non collocated multi-probe EEG is executed by applying deep learning techniques without performing any significant amount of data preprocessing and manual feature engineering. Two types of deep neural networks, deep belief network (DBN) and deep convolutional neural network (DCNN) are used at the core of the proposed framework, which automatically learns necessary features from EEG for a given classification task. Validation on extensive amount of data, which was collected from several subjects while they were performing multiple tasks (listening and watching) in PHM service training session, is presented and significant parallels are drawn from existing domain knowledge on EEG data understanding. Comparison with machine learning benchmark techniques shows that deep learning based tools are better at understanding EEG data for task classification. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite.
Condition Monitoring Technologies for Steel Wire Ropes – A Review
Authors: Schlanbusch Rune , Oland Espen , R. Bechhoefer Eric
Published: March 2021

In this research, we review condition-monitoring technologies for offshore steel wire ropes (SWR). Such ropes are used within several offshore applications including cranes for load handling such as subsea construction at depths up to 3-4000 meters, drilling lines, marine riser tensioner lines and anchor lines. For mooring, there is a clear tendency for using fiber ropes. Especially for heavy-lift cranes and subsea deployment, winches with strong ropes of up to 180 mm in diameter may be required, which has a considerable cost per rope, especially for large water depths. Today’s practice is to discard the rope after a predetermined number of uses due to fatigue from bending over sheaves with a large safety factor, especially for systems regulated by active heave compensation (AHC). Other sources of degradation are abrasion, fretting, corrosion and extreme forces, and are typically accelerated due to undersized or poorly maintained sheaves, groove type, lack of lubrication and excessive load.Non-destructive testing techniques for SWR have been developed over a period of 100 years. Most notably are the magnetic leakage techniques (electromagnetic methods), which are widely used within several industries such as mining and construction.The content reviewed in this research is primarily the developments the last five years within the topics of electromagnetic method, acoustic emissions (AE), ultrasound, X- and γ-rays, fiber optics, optical and thermal vision and current signature analysis. Each technique is thoroughly presented and discussed for the application of subsea construction. Assessments include ability to detect localized flaws (i.e. broken wire) both internally and externally, estimated loss of metallic cross sectional area, robustness with respect to the rough offshore environment, ability to evaluate both rope and end fittings, and ability to work during operation.
Prognostic systems representation in a function-based Bayesian model during engineering design
Authors: L’Her Guillaume , L. Van Bossuyt Douglas , M. O’Halloran Bryan
Published: March 2021

Prognostics and Health Management (PHM) systems are usually only considered and set up in the late stage of design or even during the system’s lifetime, after the major design decision have been made. However, considering the PHM system’s impact on the system failure probabilities can benefit the system design early on and subsequently reduce costs. The identification of failure paths in the early phases of engineering design can guide the designer toward a safer, more reliable and cost-efficient design. Several functional failure modeling methods have been developed recently. One of their advantages is to allow for risk assessment in the early stages of the design. Risk and reliability functional failure analysis methods currently developed do not explicitly model the PHM equipment used to identify and prevent potential system failures. This paper proposes a framework to optimize prognostic systems selection and positioning during the early stages of a complex system design. A Bayesian network, incorporating the PHM systems, is used to analyze the functional model and failure propagation. The algorithm developed within the proposed framework returns the optimized placement of PHM hardware in the complex system, allowing the designer to evaluate the need for system improvement. A design tool was developed to automatically apply the proposed method. A generic pressurized water nuclear reactor primary coolant loop system is used to present a case study illustrating the proposed framework. The results obtained for this particular case study demonstrate the promise of the method introduced in this paper. The case study notably exhibits how the proposed framework can be used to support engineering design teams in making better informed decisions early in the design phase.
Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation
Authors: Atamuradov Vepa , Medjaher Kamal , Dersin Pierre , Lamoureux Benjamin , Zerhouni Noureddine
Published: March 2021

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations.
Diagnosing and PredictingWind Turbine Faults from SCADA Data Using Support Vector Machines
Authors: Leahy Kevin , Lily Hu R. , C. Konstantakopoulos Ioannis , J. Spanos Costas , M. Agogino Alice , T. J. O’Sullivan Dominic
Published: March 2021

Unscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to performmaintenance before it’s needed. To date, a strong effort has been applied to developing Condition Monitoring Systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine’s Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost.In this paper, fault and alarm data from a turbine on the Southern coast of Ireland is analysed to identify periods of nominal and faulty operation. Classification techniques are then applied to detect and diagnose faults by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show recall scores generally above 80% for fault detection and diagnosis, and prediction up to 24 hours in advance of specific faults, representing significant improvement over previous techniques.
Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers
Authors: Krishnakumar P. , Rameshkumar K. , Ramachandran K. I.
Published: March 2021

To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature.
A Review of Data-Driven Oil and Gas Pipeline Pitting Corrosion Growth Models Applicable for Prognostic and Health Management
Authors: Heidary Roohollah , A. Gabriel Steven , Modarres Mohammad , Groth Katrina M. , Vahdati Nader
Published: March 2021

Pitting corrosion is a primary and most severe failure mechanism of oil and gas pipelines. To implement a prognostic and health management (PHM) for oil and gas pipelines corroded by internal pitting, an appropriate degradation model is required. An appropriate and highly reliable pitting corrosion degradation assessment model should consider, in addition to epistemic uncertainty, the temporal aspects, the spatial heterogeneity, and inspection errors. It should also take into account the two well-known characteristics of pitting corrosion growing behavior: depth and time dependency of pit growth rate. Analysis of these different levels of uncertainties in the amount of corrosion damage over time should be performed for continuous and failure-free operation of the pipelines. This paper reviews some of the leading probabilistic data-driven prediction models for PHM analysis for oil and gas pipelines corroded by internal pitting. These models categorized as random variable-based and stochastic process-based models are reviewed and the appropriateness of each category is discussed. Since stochastic process-based models are more versatile to predict the behavior of internal pitting corrosion in oil and gas pipelines, the capabilities of the two popular stochastic process-based models, Markov process-based and gamma process-based, are discussed in more detail.
Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms
Authors: Asgarpour Masoud , Dalsgaard Sørensen John
Published: March 2021

The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation monitoring, fault prediction and predictive maintenance of offshore wind components is defined.The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution with stochastic scale factor modelled by a normal distribution. Once based on failures, inspection or condition monitoring data sufficient observations on the degradation level of a component are available, using Bayes’ rule and Normal-Normal model prior exponential parameters of the degradation model can be updated. The components of the diagnostic model defined in this paper are further explained within several illustrative examples. At the end, conclusions are given and recommendations for future studies on this topic are discussed.
The specification and testing of a Horizontal Axis Tidal Turbine Rotor Monitoring approach
Authors: Allmark Matthew , Prickett Paul , Grosvenor Roger , Frost Carwyn
Published: March 2021

The sustainable deployment of Horizontal Axis Tidal Turbines will require effective management and maintenance functions. In part, these can be supported by the engineering of suitable condition monitoring systems. The development of such a system is inevitably challenging, particularly given the present limited level of operational data associated with installed turbines during fault onset. To mitigate this limitation, a computational fluid dynamics model is used to simulate the operational response of a turbine under a known set of fault conditions. Turbine rotor imbalance faults were simulated by the introduction of increasing levels of pitch angle offset for a single turbine blade. The effects of these fault cases upon cyclic variations in the torque developed by the turbine rotor were then used to aid creation of a condition monitoring approach. A parametric tidal turbine rotor model was developed based on the outputs of the computational fluid dynamics models. The model was used to facilitate testing of the condition monitoring approach under a variety of more realistic conditions. The condition monitoring approach showed good performance in fault detection and diagnosis for simulations relating to turbulence intensities of up to 2 %. Finally, the condition monitoring approach was applied to simulations of 10 % turbulence intensity. Under the 10 % turbulence intensity case the rotor monitoring approach was successfully demonstrated in its use for fault detection. The paper closes with discussion of the effectiveness of using computational fluid dynamics simulations extended by parametric models to develop condition monitoring systems for horizontal axis tidal turbine applications.
Wind Turbine Bearing Fault Detection Using Adaptive Resampling and Order Tracking
Authors: Walker Cody , Coble Jamie
Published: March 2021

Wind energy is growing increasingly popular in the United States, so it is imperative to make it as cost competitive as possible. Operations and Maintenance (O&M) make up 20-25% of the total cost of onshore wind projects. Unplanned maintenance contributes approximately 75% of the total maintenance costs (WWEA, 2012). Condition-based maintenance strategies intend to maximize the uptime by reducing to the amounts of unplanned maintenance. This should result in an overall decrease in the cost of maintenance. Wind turbines produce an interesting challenge, because their main shaft rotation is both slow and nonstationary. Through the use of adaptive resampling and order tracking, both of these challenges were combated as the bearing fault was identified in the order spectrum then tracked as it progressed. The fault was identified as an outer race defect on the main bearing that initiated sometime during or before installation. The total energy in the order spectrum around the bearing fault rate was identified as a potential front-runner for a prognostic parameter. This paper presents a case study application to operational wind turbine bearing data to demonstrate the ease and intuitiveness of combining adaptive resampling and order tracking to diagnose faults for slow, nonstationary bearings. Prognosis of remaining useful life is proposed with features extracted from the order spectrum, but additional data are needed to develop and demonstrate this analysis.
Design of Cyber-Physical Systems Architecture for Prognostics and Health Management of High-speed Railway Transportation Systems
Authors: Liu Zongchang , Zhang Zhiqiang , Xu Guanji , Jin Wenjing , Lee Jay
Published: March 2021

The high-speed railway (HSR) transportation system in China has been growing rapidly during the past decade. In 2016, the total length of HSR in China has reached to 22,000 kilometers, and there are over 2,000 pairs of high speed trains operating daily. With the advancement of design and manufacturing technologies, the reliability and construction costs have been improved significantly. However, there is still great need for reduction of their operation and maintenance costs. With such incentive, a pilot project has been launched to develop a prognostics and health management system for rolling stock to transform the maintenance paradigm from preventive to predictive maintenance. Considering the high task variety and big data environment in HSR real-time monitoring system, a cyberphysical system (CPS) architecture is proposed as the framework for its PHM system. This paper reviews the needs of predictive maintenance for the HSR system, and then present a concept design of the CPS-enabled smart operation and maintenance system.
A Review of Techniques to Mitigate Jamming in Electromechanical Actuators for Safety Critical Applications
Authors: M. Hussain Yameen , Burrow Stephen , Henson Leigh , Keogh Patrick
Published: March 2021

This paper presents a review of techniques to mitigate jamming in Electromechanical Actuators (EMA) for safety critical applications in aerospace. Published progress to date is evaluated, with the remaining challenges highlighted. Through the use of Hierarchical Process Modelling (HPM), two key approaches to mitigate jamming were identified: (1) Fault Diagnostics (FD) and (2) Fault tolerant design. The development of a fault tolerant EMA system is currently at an early stage for implementation within safety critical systems due to the increased complexity of such systems (for example the anti-jamming system may require FD functionality itself). Challenges also exist for FD approaches particularly in achieving a robust means of fault detection. It is proposed that a hybrid FD approach, using a combination of model based and data-driven techniques to predict the onset of jamming, would be beneficial in capturing the discrepancies between the predicted and observed behaviour used to isolate and identify faults. Furthermore, several aspects of modelling and of data-driven methodologies for FD in the literature omit potentially important behaviours, and recommendations are made to improve upon this. For example, the simulation of faults in test stand analysis and the fidelity modelling of the motor and mechanical components are key areas to develop.
Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling
Authors: Patange Abhishek D. , Jegadeeshwaran R
Published: March 2021

The customized usage of tool inserts plays an imperative role in the economics of machining operations. Eventually, any in-process defects in the cutting tool lead to deterioration of complete machining activity. Such defects are untraceable by the conventional practices of condition monitoring. The characterization of such in-process tool defects needs to be addressed smartly. This would also assist the requirement of ‘self-monitoring’ in Industry 4.0. In this context, induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein. The variation in faulty and fault-free tool condition is collected in terms of vibrations during the face milling process on CNC (Computer Numerically Controlled) machine tool. The statistical approach is incorporated to extract attributes and the dimensionality of the attributes is reduced using the J48 decision tree algorithm. The various conditions of tool inserts are then classified using two supervised algorithms viz. Bayes Net and Naïve Bayes from the Bayesian family.
Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations – Summary of an Interactive Workshop Highlighting PHM Trends
Authors: Weiss Brian , Brundage Michael
Published: March 2021

Personnel from the National Institute of Standards and Technology (NIST) organized and led a Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) workshop at the 2019 Annual Conference of the Prognostics and Health Management Society held on September 23rd, 2019 in Scottsdale, Arizona. This event featured panel presentations and discussions from industry, government, and academic participants who are focused in advancing monitoring, diagnostic, and prognostic (collectively known as prognostic and health management (PHM)) capabilities within manufacturing operations. The participants represented a diverse cross-section of technology developers, integrators, end-users/manufacturers (from small to large), and researchers. These contributors discussed 1) what works well, 2) common challenges that need to be addressed, 3) where the community’s priorities should be focused, and 4) how PHM technological adoption can be sped in a cost-effective manner. This report summarizes the workshop and offers lessons learned regarding the current state of PHM. Based upon the discussions, recommended next steps to advance this technological domain are also presented.
Supporting the Implementation of Predictive Maintenance
Authors: Wagner Carolin , Hellingrath Bernd
Published: March 2021

The perception of predictive maintenance as a proactive maintenance strategy to anticipate and reduce severe and costly failures and by thus increasing asset reliability has grown significantly in recent years. Due to the availability of machine sensor data and the intention to use these data in a purposeful way, the introduction of predictive maintenance provides a logical step towards maintenance optimization in industry. Several German industrial surveys highlight the growing interest in the topic by the majority of the addressed companies. Nevertheless, most of these companies are considering predictive maintenance on their future agenda and are currently only at the beginning of its implementation. This is, in many cases, due to missing internal knowledge and systematic guidance for maintenance practitioners. Existing process models and supportive guidance build on theoretical knowledge from experts; however, they often lack the complexity and challenges of industrial applications. In addition, most theoretical models focus on specific aspects of the entire process, target particular application areas, or present a few high-level steps. This paper, therefore, introduces the Process Reference Model for Predictive Maintenance (PReMMa), a comprehensive three-stage hierarchical process reference model for the implementation of predictive maintenance for industrial applications. The process reference model synthesizes existing process models as well as results from interviews with eleven practitioners from both management consultancies and experts from several industrial fields. With regard to four main phases and the predictive maintenance application, results are presented with consideration of the essential steps, their deliverables as well as the involved persons.
Evaluation of Lumbar Facet Joint Nerve
Blocks in the Management of Chronic
Low Back Pain: Preliminary Report of A
Randomized, Double-Blind Controlled Trial:
Clinical Trial NCT00355914
Authors: Manchikanti Laxmaiah
Published: March 2021

Background: The prevalence of persistent low back pain with the involvement of lumbar facet
or zygapophysial joints has been described in controlled studies as varying from 15% to 45% based
on the criteria of the International Association for the Study of Pain. Therapeutic interventions utilized in managing chronic low back pain of facet joint origin include intraarticular injections, medial
branch nerve blocks, and neurolysis of medial branch nerves.
Objective: To determine the clinical effectiveness of therapeutic lumbar facet joint nerve
blocks in managing chronic low back pain of facet joint origin.
Design: A prospective, randomized, double-blind trial.
Setting: An interventional pain management setting in the United States.
Methods: In this preliminary analysis, data from a total of 60 patients were included, with 15
patients in each of 4 groups. Thirty patients were in a non-steroid group consisting of Groups I
(control, with lumbar facet joint nerve blocks using bupivacaine ) and II (with lumbar facet joint
nerve blocks using bupivacaine and Sarapin); another 30 patients were in a steroid group consisting of Groups III (with lumbar facet joint nerve blocks using bupivacaine and steroids) and IV
(with lumbar facet joint nerve blocks using bupivacaine, Sarapin, and steroids). All patients met
the diagnostic criteria of lumbar facet joint pain by means of comparative, controlled diagnostic blocks.
Outcome Measures: Numeric Rating Scale (NRS) pain scale, the Oswestry Disability Index
2.0 (ODI), employment status, and opioid intake.
Results: Significant improvement in pain and functional status were observed at 3 months,
6 months, and 12 months, compared to baseline measurements. The average number of
treatments for 1 year was 3.7 with no significant differences among the groups. Duration of
average pain relief with each procedure was 14.8 ± 7.9 weeks in the non-steroid group, and
12.5 ± 3.3 weeks in the steroid group, with no significant differences among the groups.
Conclusion: Therapeutic lumbar facet joint nerve blocks with local anesthetic, with or without Sarapin or steroids, may be effective in the treatment of chronic low back pain of facet joint
origin.
Key words: Chronic back pain, lumbar facet joint pain, lumbar zygapophysial joint pain,
medial branch blocks, therapeutic lumbar facet joint nerve blocks, local anesthetic.
Variations in Opioid Responsiveness
Authors: Smith Howard
Published: March 2021

It has been appreciated for some time now that humans react differently to opioids. A
specific opioid such as morphine sulfate may have specific analgesic effects for certain
patients with postherpetic neuralgia whereas in other patients with postherpetic neuralgia, it may provide quite different analgesic qualities. Also, in any one individual patient
a particular opioid may provide better analgesia than other opioids. Furthermore, these
differences are not unique to analgesia; they can also be seen with other opioid effects/
toxicities. Though many of the differences can be classified neatly into pharmacokinetic and pharmacodynamic differences, there are certain differences which still remain incompletely understood. Also, clinicians are not yet able to easily predict which patients
will respond well or poorly to various opioids. As research unravels the various genetics,
biochemical, and receptor interaction differences of opioids in humans, it is hoped that
easily obtainable, cost-effective testing will become available to aid clinicians in choosing an optimal opioid analgesic for an individual patient, a process which is currently accomplished via health care provider judgment along with trial and error. In the future,
knowledge gained from databases on knockout rodents, pharmacogenetics, and gene
polymorphisms may impact on the ability of clinicians to predict patient responses to
doses of specific opioids in efforts to individualize optimal opioid analgesic therapy. It is
conceivable that eventually information of this type may translate into improved patient
care. In the future, armed with data of this type, clinicians may become quite adept at
tailoring appropriate opioid therapy as well as optimal opioid rotation strategies.
Key words: Opioids, pain, analgesic responsiveness, OPRM1 118G, muopioid receptor, gene polymorphism
Increased Knee Cartilage Volume in
Degenerative Joint Disease using
Percutaneously Implanted, Autologous
Mesenchymal Stem Cells
Authors: Busse Dan
Published: March 2021

Background: The ability to repair tissue via percutaneous means may allow
interventional pain physicians to manage a wide variety of diseases including
peripheral joint injuries and osteoarthritis. This review will highlight the developments in cellular medicine that may soon permit interventional pain management physicians to treat a much wider variety of clinical conditions and highlight
an interventional case study using these technologies
Objective: To determine if isolated and expanded human autologous mesenchymal stem cells could effectively regenerate cartilage and meniscal tissue
when percutaneously injected into knees.
Design: Case Study
Setting: Private Interventional Pain Management practice.
Methods: An IRB approved study with a consenting volunteer in which mesenchymal stem cells were isolated and cultured ex-vivo from bone marrow aspiration of the iliac crest. The mesenchymal stem cells were then percutaneously
injected into the subject’s knee with MRI proven degenerative joint disease. Preand post-treatment subjective visual analog pain scores, physical therapy assessments, and MRIs measured clinical and radiographic changes.
Results: At 24 weeks post-injection, the patient had statistically significant cartilage and meniscus growth on MRI, as well as increased range of motion and
decreased modified VAS pain scores.
Conclusion: The described process of autologous mesenchymal stem cell culture and percutaneous injection into a knee with symptomatic and radiographic degenerative joint disease resulted in significant cartilage growth, decreased
pain and increased joint mobility in this patient. This has significant future implications for minimally invasive treatment of osteoarthritis and meniscal injury.
Key words: autologous mesenchymal stem cells, osteoarthritis, knee, cartilage
Quality Assurance for Interventional Pain
Management Procedures in Private Practice
Authors: Zhou YiLi
Published: March 2021

Background: A recent study has indicated that quality assurance for interventional
pain management procedures (IPMPs) can be achieved in university pain clinics. However, the issue of quality assurance for IPMPs in private practice has not yet been addressed.
Objective: This study was designed to monitor the quality of IPMPs in a private pain
practice in north Florida.
Methods: From November 2005 to July 2006, we monitored the quality of IPMPs in
a private pain practice in north Florida. Questionnaires regarding degree of pain relief,
patient satisfaction, and complications were handed to patients immediately after the
completion of each IPMP. Follow-up phone calls were also made to patients 1 day after the IPMPs.
Results: A total of 771 (male: 249, female: 522) patients with a mean age of 58.1
years participated in the study. Office-based IPMPs included lumbar and cervical epidural steroid injections, lumbar and cervical facet joint blocks, selective nerve root blocks,
lumbar and cervical sympathetic nerve blocks, sacroiliac joint injection, and large joint
injections. Seven-hundred patients (90.8%) reported various degrees of pain relief immediately following IPMPs. Average pain score decreased by 4.3 on a 0 to 10 scale
(p=<0.001). Number needed to be treated (NNT) to reach 50% or more pain relief immediately after IPMPs was 1.4. Six-hundred ninety-two (89.7%) patients were satisfied
or very satisfied with the results of IPMPs. sixty-two patients (8%) developed headaches
after IPMPs, which lasted from 30 minutes to 4 days. None of these patients required
a blood patch. Five patients developed moderate vasovagal responses during IPMPs, in
which their heart rates decreased to <45/min, BP <90/60mmHg. The IPMPs were aborted immediately. All of these patients recovered uneventfully within a few minutes. No
other serious adverse events were reported.
Conclusions: The results of the current study suggest that high quality private interventional pain programs with high efficacy, high patient satisfaction, and low complication rates can be achieved through appropriate staff training, proper monitoring of
patients during IPMPs, and adequate handling of patients after the IPMPs.
Key words: Interventional pain management procedures, quality assurance, efficacy, patient satisfaction