bluuebunny/crossref_metadata_2025 huggingface dataset:
A Microscopic Approach for Generic Degradation Modeling
Authors: Momeni Farhang , Jin Xiaoning , Ni Jun
Published: March 2021

One of the main challenges in prognostics is degradation modeling for which there are no straightforward methods compared with system governing equations modeling (e.g., Newton`s law, Euler`s law, thermodynamics conservation laws, fluid dynamics laws, and so forth). Once both governing equations and degradation equations are generated, then the RUL or EOL can be easily estimated by using a filtering technique like Kalman filter. This paper presents a new approach for generic degradation modeling which can be engaged in (1) complex engineering systems, and (2) the structures which are fabricated by the new manufacturing processes such as 4D printing that in both cases the physical knowledge is not adequate to model the degradation equations. In the existing approaches for parametric degradation modeling, there are always possibilities that a specific degradation phenomenon of a new system is ignored. This deficiency arises from the assumption made in the previous studies that degradation phenomenon is equivalently represented by degradation mechanism such as crack, wear, corrosion, erosion, and so forth. Here we relax this assumption and provide a more general approach for parametric degradation modeling. This research first, quantifies the concepts of governing equations and degradation equations in prognostics in a general view, second provides a generic approach for deriving the governing equations by using techniques of system identification, third, it digs into the concepts of degradation and provides a microscopic approach for generic degradation modeling, and finally it combines and incorporates the governing equations and degradation equations in a way that the generality is conserved. In addition, this paper opens the door of degradation modeling in 4D printing by starting from the conceptual point of view to the final generic methodology.
Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing
Authors: O’Donovan Peter , Bruton Ken , T.J. O’Sullivan Dominic
Published: March 2021

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).
Smart Manufacturing PHM
Authors: A. Weiss Brian , Freeman Philip , Lee Jay , Pavel Radu
Published: March 2021

The age of Smart Manufacturing has arrived where more and more organizations are embracing it to innovate and maintain their competitiveness. Smart Manufacturing blends information technology (IT) with operations technology (OT) to enable greater productivity, efficiency, quality, and customization within factory operations. More specifically, emerging and existing factory-floor level technologies (including robotics, machine tools, additive processes, automation, and sensors) are being fused with networking (both wired and wireless) and analysis technologies to generate more timely, accurate, and appropriate communication. This communication directly enables more intelligent sensing, monitoring, and control of the overall manufacturing system, including its constituent processes and sub-systems. Organizations that are adopting a smart manufacturing approach have become more flexible and adaptive to address changing customer demands, integrate new technologies, mitigate supply chain disruptions, and better utilize their human workforce. Prognostics and Health Management (PHM), in the context of Smart Manufacturing, focuses on the technologies and capabilities that enable health monitoring, diagnostics, and prognostics to promote greater intelligence in maintenance and control activities.
Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder
Authors: Akintayo Adedotun , Gwn Lore Kin , Sarkar Soumalya , Sarkar Soumik
Published: March 2021

The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and having large amplitude pressure oscillations with varying spatial scales of periodic coherent vortex shedding. Early detection and close monitoring of combustion instability are the keys to extending the remaining useful life (RUL) of any gas turbine engine. However, such impending instability to a stable combustion is extremely difficult to detect only from pressure data due to its sudden (bifurcationtype) nature. Toolchains that are able to detect early instability occurrence have transformative impacts on the safety and performance of modern engines. This paper proposes an endto- end deep convolutional selective autoencoder approach to capture the rich information in hi-speed flame video for instability prognostics. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. Performance comparison is done with a wellknown image processing tool, conditional random field that is trained to be selective as well. In this context, an informationtheoretic threshold value is derived. The proposed framework is validated on a set of real data collected from a laboratory scale combustor over varied operating conditions where it is shown to effectively detect subtle instability features as a combustion process makes transition from stable to unstable region.
A Hybrid Approach for Fusing Physics and Data for Failure Prediction
Authors: Pillai Prashanth , Kaushik Anshul , Bhavikatti Shivanand , Roy Arjun , Kumar Virendra
Published: March 2021

This work describes the architecture for developing physics of failure models, derived as a function of machine sensor data, and integrating with data pertaining to other relevant factors like geography, manufacturing, environment, customer and inspection information, that are not easily modeled using physics principles. The mechanics of the system is characterized using surrogate models for stress and metal temperature based on results from multiple non-linear finite element simulations. A cumulative damage index measure has been formulated that quantifies the health of the component. To address deficiencies in the simulation results, a model tuning framework is designed to improve the accuracy of the model. Despite the model tuning, un-modelled sources of variation can lead to insufficient model accuracy. It is required to incorporate these un-modelled effects so as to improve the model performance. A novel machine learning based model fusion approach has been presented that can combine physics model predictions with other data sources that are difficult to incorporate in a physics framework. This approach has been applied to a gas turbine hot section turbine blade failure prediction example.
A SOM based Anomaly Detection Method for Wind Turbines Health Management through SCADA Data
Authors: Du Mian , Bertling Tjernberg Lina , Ma Shicong , He Qing , Cheng Lin , Guo Jianbo
Published: March 2021

In this paper, a data driven method for Wind Turbine system level anomaly detection and root sub-component identification is proposed. Supervisory control and data acquisition system (SCADA) data of WT is adopted and several parameters are selected based on physical knowledge in this domain and correlation coefficient analysis to build a normal behavior model. This model which is based on Self-organizing map (SOM) projects higher-dimensional SCADA data into a two-dimension-map. Afterwards, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. In order to track which sub-component should be responsible for an anomaly, a contribution proportion (CP) index is proposed. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method.
Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine
Authors: W. Gregg Seth , P.H. Steele John , L. Van Bossuyt Douglas
Published: March 2021

This paper presents a method for comparing and evaluating cavitation detection features - the first step towards estimating remaining useful life (RUL) of hydroturbine runners that areimpacted by erosive cavitation. The method can be used to quickly compare features created from cavitation survey data collected on any type of hydroturbine, sensor type, sensor location, and cavitation sensitivity parameter (CSP). Although manual evaluation and knowledge of hydroturbine cavitation is still required for our feature selection method, the use of principal component analysis greatly reduces the number of plots that require evaluation. We present a case study based on a cavitation survey data collected on a Francis hydroturbine located at a hydroelectric plant and demonstrate the selection of the most advantageous sensor type, sensor location, and CSP to use on this hydroturbine for long-term monitoring of erosive cavitation. Our method provides hydroturbine operators and researchers with a clear and effective means to determine preferred sensors, sensor placements, and CSPs while also laying the groundwork for determining RUL in the future.
Decentralized Fault Diagnosis and Prognosis Scheme for Interconnected Nonlinear Discrete-Time Systems
Authors: Ferdowsi H. , Jagannathan S.
Published: March 2021

This paper deals with the design of a decentralized fault diagnosis and prognosis scheme for interconnected nonlinear discrete-time systems which are modelled as the interconnection of several subsystems. For each subsystem, a local fault detector (LFD) is designed based on the dynamic model of the local subsystem and the local states. Each LFD consists of an observer with an online neural network (NN)-based approximator. The online NN approximators only use local measurements as their inputs, and are always turned on and continuously learn the interconnection as well as possible fault function. A fault is detected by comparing the output of each online NN approximator with a predefined threshold instead of using the residual. Derivation of robust detection thresholds and fault detectability conditions are also included. Due to interconnected nature of the overall system, the effect of faults propagate to other subsystems, thus a fault might be detected in more than one subsystem. Upon detection, faults local to the subsystem and from other subsystems are isolated by using a central fault isolation unit which receives detection time information from all LFDs. The proposed scheme also provides the time-to-failure or remaining useful life information by using local measurements. Simulation results provide the effectiveness of the proposed decentralized fault detection scheme.
Windings Fault Detection and Prognosis in Electro-Mechanical Flight Control Actuators Operating in Active-Active Configuration
Authors: De Martin Andrea , Jacazio Giovanni , Vachtsevanos George
Published: March 2021

One of the most significant research trends in the last decades of the aeronautic industry is the effort to move towards the design and the production of “more electric aircraft”. Within this framework, the application of the electrical technology to flight control systems has seen a progressive, although slow, increase: starting with the introduction of fly-by-wire and proceeding with the partial replacement of the traditional hydraulic/electro-hydraulic actuators with purely electro-mechanical ones. This evolution allowed to obtain more flexible solutions, reduced installation issues and enhanced aircraft control capability. Electro-Mechanical Actuators (EMAs) are however far from being a mature technology and still suffer from several safety issues, which can be partially limited by increasing the complexity of their design and hence their production costs. The development of a robust Prognostics and Health Management (PHM) system could provide a way to prevent the occurrence of a critical failure without resorting to complex device design. This paper deals with the first part of the study of a comprehensive PHM system for EMAs employed as primary flight control actuators; the peculiarities of the application are presented and discussed, while a novel approach, based on short pre-flight/post-flight health monitoring tests, is proposed. Turn-to-turn short in the electric motor windings is identified as the most common electrical degradation and a particle filtering framework for anomaly detection and prognosis featuring a self-tuning non-linear model is proposed. Features, anomaly detection and a prognostic algorithm are hence evaluated through state-of-the art performance metrics and their results discussed.
Incorporating Human Reliability Analysis to enhance Maintenance Audits: The Case of Rail Bogie Maintenance
Authors: Singh Sarbjeet , Majumdar Arnab , Kyriakidis Miltos
Published: March 2021

Human errors occurring during railway maintenance activities can significantly reduce the availability of equipment. Identification of potential human errors, their causes and prediction of the associated probabilities are important stages in order to manage such errors. This paper investigates the probability of human error during the maintenance of railway bogies. A case study examines technicians performing maintenance on the disc brake assembly unit, wheel set, and bogie frame under various error producing conditions in a railway maintenance workshop in Luleå, Sweden. The Human Error Assessment and Reduction Technique (HEART) is employed to determine the probability of human error occurring during each of the maintenance tasks, while fault tree analysis is used to define the potential errors throughout the maintenance process. The probability of a technician committing an error during the maintenance of the disc brake assembly, wheel set, and bogie frame is found to be 0.20, 0.039 and 0.021 respectively, with the human error probability (HEP) for the entire bogie 0.24. Time pressure, ability to detect and perceive problems, over-riding information, the need to make decisions and mismatches between the operator and designer’s model turn out to be major contributors to human error. These findings can help maintenance management personnel to better understand the error producing conditions that may lead to errors and in turn serve as an input to modify policies and guidelines for railway maintenance tasks.
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
Authors: Gugulothu Narendhar , TV Vishnu , Malhotra Pankaj , Vig Lovekesh , Agarwal Puneet , Shroff Gautam
Published: March 2021

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art (Malhotra, TV, et al., 2016) on several metrics.
Traffic sensor health monitoring using spatiotemporal graphical modeling
Authors: Wu Linjiang , Liu Chao , Huang Tingting , Sharma Anuj , Sarkar Soumik
Published: March 2021

Accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the traffic sensors (e.g., wide range detector, automatic traffic recorder). Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision is required. With the purpose of efficiently determining the sensor health status and identifying the failed sensor(s) in a timely manner, this paper proposes a graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this paper to formulate and analyze the proposed sensor health monitoring system and historical time-series data from a network of traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, we demonstrate that the proposed approach can: (i) extract spatiotemporal dependencies among the different sensors which leads to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network.
Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model
Authors: Kumar Pandit Ravi , Infield David
Published: March 2021

Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine.Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process developed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then was compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a standard issue with a wind turbine, which causes underperformance. Hence it is used as case study to test and validate the algorithm effectiveness.
PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes
Authors: Soualhi Abdenour , Hawwari Yasmine , Medjaher Kamal , Clerc Guy , Hubert Razik , Guillet François
Published: March 2021

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.
TDR-based Multiple Leak Detection System using an S-parameter Transmission Line Model for Long-Distance Pipelines
Authors: Woo Sihyeong , Kim Taejin , Lee Junmin , Oh Hyunseok , Youn Byeng D. , Kwon Daeil
Published: March 2021

Leaks in water distribution systems should be detected to avoid economic, environmental, and social problems. Existing Bayesian Inference based time-domainreflectometry (TDR) methods for leak detection have a limitation for real applications due to the lengthy time in building sample data. As the pipeline distance becomes longer and multiple leaks must be considered in long distance pipelines, the computational time for building training data gets larger. This paper proposes a scattering-parameter-based forward model to relieve computational burden of the existing TDR methods. It was shown that the proposed model outperformed the existing RLGC-based forward model in terms of computational time. The proposed model that is combined with Bayesian inference and TDR signal modeling is validated with an experimental pipeline, leak detectors, transmission line, and TDR instrument for leak detection. In summary, the proposed method is promising for leak detection in long pipelines as well as multiple leaks.
Enhancing Decisions in Prognostics and Health Management Framework
Authors: Bougacha Omar , Varnier Christophe
Published: March 2021

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplifiedexample is developed to illustrate the presented methodology of post-prognostic decision enhancement.
Remaining Useful Life Estimation of Bearings
Authors: Ferreira Hugo M. , De Sousa Alexandre C.
Published: March 2021

In the domain of predictive maintenance, when trying to repli- cate and compare research in remaining useful life estimation (RUL), several inconsistencies and errors were identified in the experimental methodology used by various researchers. This makes the replication and the comparison of results diffi- cult, thus severely hindering both progress in this research do- main and its practical application to industry. We survey the literature to evaluate the experimental procedures that were used, and identify the most common errors and omission in both experimental procedures and reporting.

A total of 70 papers on RUL were audited. From this meta- analysis we estimate that approximately 11% of the papers present work that will allow for replication and comparison. Surprisingly, only about 24.3% (17 of the 70 articles) com- pared their results with previous work. Of the remaining work, 41.4% generated and compared several models of their own and, somewhat unsettling, 31.4% of the researchers made no comparison whatsoever. The remaining 2.9% did not use the same data set for comparisons. The results of this study were also aggregated into 3 categories: problem class selec- tion, model fitting best practices and evaluation best practices. We conclude that model evaluation is the most problematic one.

The main contribution of the article is a proposal of an ex- perimental protocol and several recommendations that specif- ically target model evaluation. Adherence to this protocol should substantially facilitate the research and application of RUL prediction models. The goals are to promote the collab- oration between scholars and practitioners alike and advance the research in this domain.
Requirements and Data Integrity Considerations for Diagnostics Testbeds
Authors: Bardakis Ioannis , Niculita Octavian , Wallace Peter
Published: March 2021

The process of generating high quality data for the test and evaluation of diagnostic and prognostic algorithms is still of high importance to the Prognostics and Health Management (PHM) research community. To support these efforts a testbed has been designed, manufactured and commissioned. It has specifically been designed in order to replicate several component degradation faults with high accuracy and high repeatability. This paper documents the design, requirements and the data integrity elements of this benchmark hydraulic system. This document consolidates the process of designing diagnostics testbeds as at present there is a lack of literature on how diagnostics testbeds should be built and is intended to serve as a starting point and quick reference guide for engineers and researchers intending to design and develop a testbed to test and validate PHM applications. The first part of this paper highlights design requirements for all the design aspects for such testbeds with great consideration for industry standards and best practices covering the achievement of electromagnetic compatibility (EMC) and noise mitigation, as well as operators’ safety and equipment protection. The second part of the paper put great emphasis on data integrity elements of the data generated by this testbed (describing the system under healthy and faulty conditions) before it is actually used for system characterization or by diagnostics and prognostics algorithms.
Review of Post-Prognostics Decision-Making in Prognostics and Health Management
Authors: Bougacha Omar , Varnier Christophe , Zerhouni Noureddine
Published: March 2021

Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.
Data-Driven Framework for Team Formation for Maintenance Tasks
Authors: Reslan Maya , Hastings Emily M. , Brundage Michael P. , Sexton Thurston
Published: March 2021

Even as maintenance evolves with new technologies, it is still a heavily human-driven domain; multiple steps in the maintenance workflow still require human expertise and intervention. Various maintenance activities require multiple maintainers, all with different skill sets and expertise, and from various positions and levels within the organization. Responding to maintenance requests, training exercises, or executing larger maintenance projects all can require maintenance teams. Having the correct assortment of individuals both in terms of skills and management experience can help improve the efficiency of these maintenance tasks. This paper presents a workflow for creating teams of maintainers by adapting accepted practices from the human-computer interaction (HCI) community. These steps provide a low-cost solution to help account for the needs of maintainers and their management, while matching skills of the maintainers with the needs of the activity.
ANALISIS AFIKSASI DALAM ALBUM “DEKADE” LAGU AFGAN
Authors: Fadhila Aulia Zahra
Published: March 2021

The research conducted was entitled "Affixation Analysis in the DEKADE Afgan Song Album". This research was conducted using qualitative methods. The data used in the study were taken from the lyrics of the Afgan song on the album "DEKADE" which is available on sites on the internet. The problem discussed was the use of affixes to Afgan's song lyrics in DEKADE's album. The results of the research conducted show that there are many uses of affixation in the lyrics of agu Afgan. There are 112 words that contain affixations in the Afgan song lyrics on the album. Of the 112 data found with affixes, suffixes had the highest percentage of usage. There were 69 data suffixes that were found, consisting of the suffixes kan-, -nya, -an, and -i. The suffix that is mostly found is kan-, while the suffix with the smallest frequency of occurrence is the suffix -an. Like suffixes, prefixes are also found in the lyrics of the song. The prefix found in 24 data consisted of tar, ber-, di-, mem-, and se-. Meanwhile, in the confixes of the song lyrics, there are 9 data consisting of performances and occasional data.
The impact of ethics and fraud pentagon theory on academic fraud behavior
Authors: Wira Utami Dhita Permata , Purnamasari Dian Indri
Published: March 2021

This study aims to determine the impact of ethics, pressure, opportunity, rationalization, competence, and arrogance on accounting students' academic fraud behavior. The population of this research consists of UPN "Veteran" Yogyakarta accounting students. The number of samples used in this study was 170 respondents representing several criteria and have taken the Auditing I and Business Ethics courses. The dependent variable (Y) in this study is academic fraud behavior. The independent variables include ethics (X1), pressure (X2), opportunity (X3), rationalization (X4), competence (X5), and arrogance (X6). The method used in this research is the quantitative method. The data used were the primary data. The results of this study indicate that ethics, pressure, and competence have an impact on academic fraud behavior. In contrast, opportunities, rationalization, and arrogance do not affect academic fraud behavior.
Meningkatkan penerapan SAK EMKM dengan persepsi usaha dan kesiapan pelaku UMKM
Authors: Purnomo Asri , Adyaksana Rahandhika
Published: March 2021

UMKM (Micro Small Medium Enterprises) plays an important role in developed countries, particularly in Indonesia, which works absorbing many workers to reduce the number of unemployment. This research aims to examine the effect on the application of accounting recording systems, accounting comprehensions, business perceptions, and UMKM preparedness in implementing SAK EMKM. The objects of the research are UMKM in Yogyakarta. The respondents were 120 UMKM owners. This study is using multiple regression analysis to examine the hypothesis. The research method uses purposive sampling under the criteria in UU No. 20 Tahun 2008 tentang Kriteria UMKM. The result indicates that the hypothesis of the accounting recording system and comprehensions does not affect the implementation of SAK EMKM. Meanwhile, the hypothesis of business perceptions and UMKM preparedness has significant positive effect on the implementation of SAK EMKM.
An Updated Systematic Review of the
Diagnostic Utility of Selective Nerve Root
Blocks
Authors: Datta Sukdeb
Published: March 2021

Background: Selective nerve root blocks or transforaminal epidural injections are used for
diagnosis and treatment of different spinal disorders. A clear consensus on the use of selective
nerve root injections as a diagnostic tool does not currently exist. Additionally, the effectiveness
of this procedure as a diagnostic tool is not clear. A systematic review of diagnostic utility of
selective nerve root blocks was performed and published in January 2005, which concluded that
selective nerve root injections may be helpful as a diagnostic tool in evaluating spinal pain with
radicular features, but its role needs to be further clarified.
Objective: To evaluate and update the accuracy of selective nerve root injections in diagnosing
spinal disorders.
Study Design: A systematic review of selective nerve root blocks for the diagnosis of spinal
pain.
Methods: A systematic review of the literature for clinical studies was performed to assess
the accuracy of selective nerve root injections in diagnosing spinal pain. Methodologic quality
evaluation was performed utilizing Agency for Healthcare Research and Quality (AHRQ) and Quality
Assessment Studies of Diagnostic Accuracy (QUADAS) criteria. Studies were graded and evidence
classified into 5 levels: conclusive, strong, moderate, limited, or indeterminate. An extensive
literature search was performed utilizing resources from the library at Vanderbilt University Medical
Center, PubMed, EMBASE, BioMed, and Cochrane Reviews. Manual searches of bibliographies of
known primary and review articles, and abstracts from scientific meetings within the last 2 years
were also reviewed.
Results: There is limited evidence on the effectiveness of selective nerve root injections as
a diagnostic tool for spinal pain. There is insufficient research for stronger support, but the
available literature is supportive of selective nerve root injections as a diagnostic test for equivocal
radicular pain. There is moderate evidence for use in the preoperative evaluation of patients with
negative or inconclusive imaging studies. The positive predictive value of diagnostic selective
nerve root blocks is low, but they have a useful negative predictive value.
Conclusion: Selective nerve root injections may be helpful as a diagnostic tool in evaluating
spinal pain with radicular features. However, their role needs to be further clarified by additional
research and consensus.
Keywords: Selective nerve root block, transforaminal epidural injection, spinal pain, discogenic
pain, radiculopathy, nerve root pain
Seven Zoster Reactivations in an Immune
Competent Patient: How Many Is Too Many?
Authors: Rashid Rashid M.
Published: March 2021

Herpes zoster virus (HZV) reactivation is commonly reported in elderly or immune compromised patients. However, certain presentations are rarely reported in immune competent patients. Here we report a rare case of recalcitrant HZV infection, including 7 reactivations over 3
years, in an immune competent patient. Due to the pain experienced, this patient self-referred
to our clinic, and thus bypassed other specialties. As pain management specialists, it is critical
to be aware of such presentations, the clinical implications involved, and management options
to consider.
Key words: Zoster, herpes zoster, zoster reactivation, shingles, post-herpetic neuralgia, Protein S deficiency, epidural analgesia
Neuromodulation of the Cervical Spinal Cord
in the Treatment of Chronic Intractable Neck
and Upper Extremity Pain: A Case Series and
Review of the Literature
Authors: Vallejo Ricardo
Published: March 2021

Electrical spinal neuromodulation in the form of spinal cord stimulation is currently used
for treating chronic painful conditions such as complex regional pain syndrome, diabetic
neuropathy, postherpetic neuralgia, peripheral ischemia, low back pain, and other conditions refractory to more conservative treatments. To date, there are very few published
reports documenting the use of spinal cord stimulation in the treatment of head/neck
and upper limb pain. This paper reports a case series of 5 consecutive patients outlining
the use of spinal cord stimulation to treat upper extremity pain. All subjects had previously undergone cervical fusion surgery to treat chronic neck and upper limb pain. Patients were referred following failure of the surgery to manage their painful conditions.
Spinal cord stimulators were placed in the cervical epidural space through a thoracic needle placement. Stimulation parameters were adjusted to capture as much of the painful area(s) as possible. In total, 4 out of 5 patients moved to implantation. In all cases,
patients reported significant (70–90%) reductions in pain, including axial neck pain and
upper extremity pain. Interestingly, 2 patients with associated headache and lower extremity pain obtained relief after paresthesia-steering reportedly covered those areas.
Moreover, 2 patients reported that cervical spinal cord stimulation significantly improved
axial low back pain. Patients continue to report excellent pain relief up to 9 months following implantation. This case series documents the successful treatment of neck and
upper extremity pain following unsuccessful cervical spine fusion surgery. Given this initial success, prospective, controlled studies are warranted to more adequately assess
the long term utility and cost effectiveness of electrical neuromodulation treatment of
chronic neck and upper extremity pain.
Key words: spinal cord stimulator, cervical, neck pain, radicular pain, axial pain, headache, leg pain
Case Series on Chronic Whiplash Related
Neck Pain Treated with Intraarticular
Zygapophysial Joint Regeneration Injection
Therapy
Authors: Hooper R. Allen
Published: March 2021

Background: Although in clinical use, there is only 1 published case report on the efficacy of intraarticular regeneration injection therapy (RIT) (a.k.a. prolotheraphy). This report supports a rationale for future clinical trials of this technique.
Objective: To assess the efficacy of intraarticular zygapophysial joint RIT in patients
with chronic whiplash related neck pain that failed other conservative and interventional
procedures. Patients were treated with intraarticular RIT and reassessed over 1 year.
Design: Retrospective case review of prospective data.
Materials and Methods: Eighteen consecutive patients were treated with
intraarticular prolotherapy by placing 0.5 – 1mL of 20% dextrose solution into each zygapophysial joint, after confirmation of intraarticular location with radiographic contrast,
using 25-gauge spinal needles and fluoroscopic guidance. Solution was prepared by diluting D50W with 1% lidocaine.
Results: Fifteen patients completed treatment. Three patients had bilateral treatment, leaving 18 sides for analysis. Mean Neck Disability Index (NDI) pre-treatment was
24.71 and decreased post-treatment to 14.21 (2 months), 13.45 (6 months), 10.94 (12
months). Average change NDI=13.77 (p<0.0001) baseline versus 12 months. Symptoms
for 14 patients were from motor vehicle accident, of which 13 were in litigation. Patients
attending physiotherapy over the course of treatment had better outcomes than those
without physiotherapy. Women needed more injections (5.4) than men (3.2) p=0.0003.
Conclusion: Intraarticular RIT improved pain and function in this case series. The procedure appears safe, more effective than periarticular RIT, and lasted as long, or longer,
than those patients with previous radiofrequency neurotomy. Concurrent physiotherapy
helped reduce post-procedure neck stiffness. Future trials should consider gender when
deciding how many treatments to administer. Litigation was not a barrier to recovery.
Key words: regenerative injection therapy, RIT, prolotherapy, zygapophysial joint, motor vehicle accident, whiplash
National Drug Control Policy and Prescription
Drug Abuse: Facts and Fallacies
Authors: Manchikanti Laxmaiah
Published: March 2021

In a recent press release Joseph A. Califano, Jr., Chairman and President of the National
Center on Addiction and Substance Abuse at Columbia University called for a major shift in
American attitudes about substance abuse and addiction and a top to bottom overhaul in
the nation’s healthcare, criminal justice, social service, and eduction systems to curtail the
rise in illegal drug use and other substance abuse.
Califano, in 2005, also noted that while America has been congratulating itself on curbing
increases in alcohol and illicit drug use and in the decline in teen smoking, abuse and addition of controlled prescription drugs-opioids, central nervous system depressants and stimulants-have been stealthily, but sharply rising. All the statistics continue to show that prescription drug abuse is escalating with increasing emergency department visits and unintentional
deaths due to prescription controlled substances.
While the problem of drug prescriptions for controlled substances continues to soar, so are
the arguments of undertreatment of pain. The present state of affairs show that there were
6.4 million or 2.6% Americans using prescription-type psychotherapeutic drugs nonmedically in the past month. Of these, 4.7 million used pain relievers. Current nonmedical use
of prescription-type drugs among young adults aged 18-25 increased from 5.4% in 2002
to 6.3% in 2005. The past year, nonmedical use of psychotherapeutic drugs has increased
to 6.2% in the population of 12 years or older with 15.172 million persons, second only to
marijuana use and three times the use of cocaine.
Parallel to opioid supply and nonmedical prescription drug use, the epidemic of medical
drug use is also escalating with Americans using 80% of world’s supply of all opioids and
99% of hydrocodone.
Opioids are used extensively despite a lack of evidence of their effectiveness in improving
pain or functional status with potential side effects of hyperalgesia, negative hormonal and
immune effects, addiction and abuse. The multiple reasons for continued escalation of prescription drug abuse and overuse are lack of education among all segments including physicians, pharmacists, and the public; ineffective and incoherent prescription monitoring programs with lack of funding for a national prescription monitoring program NASPER; and a
reactive approach on behalf of numerous agencies.
This review focuses on the problem of prescription drug abuse with a discussion of facts and
fallacies, along with proposed solutions.
Key words: Prescription drug abuse, opioid abuse, opioid misuse, National Drug Control
Policy, NASPER, prescription drug monitoring programs.
Spread of Contrast During L4 and L5 Nerve
Root Infiltration Under Fluoroscopic Guidance
Authors: Vassiliev Dmitri
Published: March 2021

Background: Lumbar selective nerve root blocks have been performed to establish the
origin of lumbar radiculopathy in clinically difficult cases. The diagnostic ability of selective nerve root blocks remains controversial because of concern over potential spread of
an injectate onto adjacent structures.
Objective: To investigate the spread of different volumes of water-soluble contrast during L4 and L5 selective nerve root blocks.
Design: Retrospective, observational case series.
Methods: Analysis of medical records and X-ray images obtained during L4 and L5 selective nerve root blocks.
Results: During L4 selective nerve root block 1 ml of contrast spread onto L5 nerve
roots in 46.1% of subjects and during L5 nerve root block 1 ml of contrast spread onto
S1 nerve root in 57.7%. There was statistically significant difference (p<0.0001) between spread of contrast onto the medially located nerve root in the same lumbar segment and nerve roots in the lumbar segment above.
Conclusions: Injection of 1 ml of contrast under fluoroscopic guidance does not guarantee selective spread of the contrast around L4 or L5 nerve roots only. There is also
spread toward the more medial nerve root in the same spinal segment during L4 and
L5 nerve root infiltration. These findings suggest that it is possible to differentiate between L4 and L5 nerve root pathology using a sequential nerve root blocks under fluoroscopic guidance.
Key words: Selective nerve root block, Lumbar radiculopathy, Fluoroscopic guidance
Ketamine: An Introduction for the Pain
and Palliative Medicine Physician
Authors: Okon Tomasz
Published: March 2021

A history of an escalating chronic intractable pain in a patient with cryoglobulinemia, vasculitis, and severe cutaneous ulcerations is presented. A strategy
of progressive, multi-agent, N-methyl-D-aspartate-receptor (NMDA-R) blockade
that resulted in adequate pain control and a three-fold reduction in opioid consumption is described. Diagnostic process of neuropathic pain and the role of
NMDA-R in the development of hyperalgesia are briefly reviewed. Thereafter, existing clinical literature describing the use of Ketamine, a major NMDA-R antagonist for management of malignant pain, is reviewed. Lastly, evidence-based original protocol for intravenous adjuvant Ketamine analgesia for severe cancer pain
is presented.
Key Words: Cancer, Ketamine, pain
Lumbar Retrodiscal Transforaminal Injection
Authors: Jasper Joseph F.
Published: March 2021

Background: Spinal injections are commonly used to treat lumbar radiculitis and back
pain. Delivery of medication to specific targeted pathology is considered important for
a successful therapeutic outcome. A variety of routes of injection have been devised for
epidural injection of corticosteroid.
Objectives: The author demonstrates a variation of the transforaminal injection technique. The radiographic spread of contrast is described using a more oblique and ventral
caudad approach in the epidural space “retrodiscal.” It is suggested that the radiographic findings of this technique for discogenic causes of induced radiculitis and/or back pain
may yield more precise targeting of putative pathologic sources of radiculopathy and
back pain in selected patients.
Methods: In patients with disc pathology and radiculitis, the anatomy of the lumbar epidural space is reviewed for its potential effect on the flow of injectate. Contrast
spread was documented for lumbar transforaminal injection using a needle placement
more oblique and behind the disc rather than in the cranial portion. Comparison is made
to a typical contrast spread of an infra-pedicular placed transforaminal injection.
Results: Retrodiscal contrast injection results in reliable coverage of the retrodiscal region, the exiting nerve at that foraminal level and the proximal portion of the transiting
segmental neural sleeve.
Conclusions: The radiographic findings demonstrate a difference between classic infra-pedicular versus retrodiscal transforaminal epidural contrast injection patterns, particularly at relatively low volumes. The clinical advantage of one technique versus the
other should be established in randomized prospective studies.
Key words: Spinal, injection, corticosteroid, adhesions, disc, herniation, radiculitis,
pain, lumbar, transforaminal, radicular, discogenic, retrodiscal, contrast