bluuebunny/crossref_metadata_2025 huggingface dataset:
Controlling Tracking Performance for System Health Management - A Markov Decision Process Formulation
Authors: Bole Brian , Goebel Kai , Vachtsevanos George
Published: March 2021

After an incipient fault mode has been detected a logical question to ask is: How long can the system continue to be operated before the incipient fault mode degrades to a failure condition? In many cases answering this question is complicated by the fact that further fault growth will depend on how the system is intended to be used in the future. The problem is then complicated even further when we consider that the future operation of a system may itself be conditioned on estimates of a system’s current health and on predictions of future fault evolution. This paper introduces a notationally convenient formulation of this problem as a Markov decision process. Prognostics-based fault management policies are then shown to be identified using standard Markov decision process optimization techniques. A case study example is analyzed, in which a discrete random walk is used to represent time-varying system loading demands. A comparison of fault management policies computed with and without future uncertainty is used to illustrate the limiting effects of model uncertainty on prognostics-informed fault management policies.
A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator
Authors: Tabrizi Ali , Garibaldi Luigi , Fasana Alessandro , Marcchesiello Stefano
Published: March 2021

Although Ensemble empirical mode decomposition (EEMD) method has been successfully applied to various applications, features extracted using EEMD could not detect anomalies for roller bearings, especially when anomalies includes small defects. In this study a novel feature extraction method is proposed to detect the state of roller bearings. Performance improved EEMD, which is a reliable adaptive method to calculate an appropriate noise amplitude is applied to decompose the acceleration signals into zero-mean components called intrinsic mode functions (IMFs). Then, three dimensional feature vectors are created by applying the Teager-Kaiser energy operator (TKEO) to the first three IMFs. The novel features obtained from the healthy bearing signals are utilized to construct the separating hyperplane using one-class support vector machine (SVM). In order to validate the method proposed, a number of operating conditions (shaft speed and load) are considered to generate the data (vibration signals) by means of an assembled test rig. It is shown that the proposed method can successfully identify the states of the new samples (healthy and faulty). The uncertainty of the model prediction is investigated computing Margin and the number of support vectors. It create less complex (less fraction of support vectors) and more reliable (higher Margin) hyperplane than the EEMD method.
On the Practical Performance of Minimal Hitting Set Algorithms from a Diagnostic Perspective
Authors: Pill Ingo , Quaritsch Thomas , Wotawa Franz
Published: March 2021

Minimal hitting sets (MHSs) meliorate our reasoning in many applications, including AI planning, CNF/DNF conversion, and program debugging. When following Reiter’s ”theory of diagnosis from first principles”, minimal hitting sets are also essential to the diagnosis problem, since diagnoses can be characterized as the minimal hitting sets of conflicts in thebehavior of a faulty system. While the large amount of application options led to the advent of a variety of corresponding MHS algorithms, for diagnostic purposes we still lack a comparative evaluation assessing performance characteristics. In this paper, we thus empirically evaluate a set of complete algorithms relevant for diagnostic purposes in synthetic andreal-world scenarios. We consider in our experimental evaluation also how cardinality constraints on the solution space, as often established in practice for diagnostic purposes, influence performance in terms of run-time and memory usage.
Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network
Authors: Abdulhady Jaber Alaa , Bicker Robert
Published: March 2021

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot.
Big Data Analytics in Online Structural Health Monitoring
Authors: Cai Guowei , Mahadevan Sankaran
Published: March 2021

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage
Novel In-Service Combustion Instability Detection Using the Chirp Fourier Higher Order Spectra
Authors: Gelman Len , Parrish Colin , Petrunin Ivan , Walters Mark
Published: March 2021

Combustion instabilities, known as “rumble” and “screech” are the self-excited aerodynamic instabilities in the gas turbine combustor. They cause the premature failures of the gas turbine components, and, consequently, the failure of the gas turbine as a whole. Because of the complex physical effects underlying the rumble and the screech phenomena, it is difficult to eliminate them completely at the design stage. Therefore, special attention should be paid to the detection of the combustion instabilities in the gas turbine in order to prevent its prolonged operation in this mode. There are known techniques, which are able to detect the rumble and the screech in gas turbines. Most of them do not consider the combustion instabilities as non-linear and non-stationary events and, therefore, have lower detection efficiency. Novel technique for in-service combustion instability detection is implemented in this paper. This technique overcomes the limitations of the existing solutions.
Exact and heuristic algorithms for post prognostic decision in a single multifunctional machine
Authors: Ladj Asma , Varnier Christophe , Benbouzid Si Tayeb Fatima , Zerhouni Noureddine
Published: March 2021

Prognostic and Health Management (PHM) benefits are strongly tied to the decision-making that follows the assimilation and interpretation of prognostics information. Hence, we deal inthis study with the post prognostic decision making in order to improve system safety and avoid downtime and inopportune maintenance spending. We investigate the problem of scheduling production jobs in a single multifunctional machine subjected to predictive maintenance based on PHM results. For this reason, we propose a new interpretation of PHM outputs to define the machine degradation corresponding to each job. We develop a Mixed Integer Linear Programming (MILP) model to find the best integrated scheduling that optimizes the total maintenance cost. Unfortunately, the MILP is not able to compute the optimal solution for large instances. Therefore, we design a Prognostic based Genetic Algorithm (Pro-GA). Computational results of different benchmarks setup show the efficiency and robustness of our scheme with an average deviation of about 0.2% over a newly proposed lower bound.
Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring
Authors: Hasegawa Takanori , Ogata Jun , Murakawa Masahiro , Kobayashi Tetsunori , Ogawa Tetsuji
Published: March 2021

Adaptive training of a vibration-based anomaly detector for wind turbine condition monitoring system (CMS) is carried out to achieve high-performance detection from the early stages of monitoring. Machine learning-based wind turbine CMSs are required to collect large-scale data to yield reli-able predictions. Existing studies in this area have postulated that both data for training a monitoring system and those during the operation of the system are obtained from identical devices. In addition, constant monitoring of data is desirable, but in practice, the data can be observed periodically (e.g., several tens of seconds of data are observed every two hours). In this case, collecting sufficient data is time consuming, making it difficult to conduct accurate predictions at the early stage of the CMS operation. To address this problem, a small amount of vibration data observed at a target wind turbine is utilized to adapt the anomaly detector that is trained on relatively large-scale vibration signals obtained from other wind turbines. In the present study, maximum a posteriori (MAP) adaptation is applied to a Gaussian mixture model (GMM)-based anomaly detector. Experimental comparisons using vibration data from the gearbox in the ex- perimental environment and those used in the wind turbine demonstrated that MAP-based GMM adaptation yielded an improvement in anomaly detection accuracy even when only a small amount of data is observed at the target gearbox.
Multi-physics based Simulations of an Oleo-pneumatic Shock Absorber System for PHM
Authors: Lok S. Krishna , M. M. Siddiqui Khazi
Published: March 2021

The paper presents multi-physics (Mechanical, Thermal, Hydraulic, and Pneumatic) based modelling and simulation of an Oleo-pneumatic shock absorber with fault capabilities. The fault simulated in this model is leakage due to eccentricity. The one-dimensional shock absorber system models to give loads at different sink velocities. These load values, used in the structural model to do static stress analysis. By using these loads directly from the system model eliminates the error in load computation from the load’s group, thereby eliminating the time and cost involved in this activity. The models and static stress analyses carried out with both 1-D and 3-D elements. The 3-D landing gear model meshed with using both auto and manual mesh generation options. The consequences of both 1-D and 3-D models mesh generation, discussed in this paper. The static stress analysis, compared with the experimental results and it is found that the results are within 5% deviation. Based on the static stress and fatigue analysis computed the life of a landing gear.
System Independent Fault Diagnosis for Synchronous Generator
Authors: Gandhi Jeet , Gopinath R. , Kumar C. Santhosh
Published: March 2021

Creating a unified fault diagnosis model that can detect faults across systems with different ratings (system independent fault diagnosis) would be of great interest in making condition-based maintenance (CBM) more popular. In this work, three phase synchronous generators with 3 and 5 kVA ratings are used for detecting stator inter-turn short circuit faults.Our baseline is a 3 kVA generator working at 1 A load during training and testing, to emulate the system/load dependent fault diagnosis. We obtained a classification accuracy of 99.75%, 100% and 100% for R phase, Y phase and B phase faults respectively. Subsequently, we evaluated the system for its load independent performance. Performance accuracy deteriorated due to the load specific variations (LSV) in the input feature vector (IFV). LSV is undesired, and we used nuisance attribute projection (NAP) to remove them. Using NAP, we obtained a performance improvement of 23.13%, 17.75% and 20.72% for three fault models on the 3 kVA generator and similar performance improvement was obtained for 5 kVA generator also.Further, we experimented for load and system independent fault diagnosis. In this case, we consider LSV and system specific variations (SSV) on IFV as undesired. We experimented with two types of NAP, (1) single step NAP, (2) stacked NAP. Experimental results show that the two staged stacked NAP outperforms. We obtained an improvement of 23.99%, 16.06% and 28.39%, in classification accuracy for three fault models, resulting in overall classification accuracy of 89.22%, 94.67% and 94.59% for R phase, Y phase and B phase fault models respectively.
Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach
Authors: Di Yuan , Jia Xiaodong , Lee Jay
Published: March 2021

As an essential process in semiconductor manufacturing, Chemical Mechanical Planarization has been studied in recent decades and the material removal rate has been proved to be a critical performance indicator. Comparing with after-process metrology, virtual metrology shows advantages in production time saving and quick response to the process control. This paper presents an enhanced material removal rate prediction algorithm based on an integrated model and data-driven method. The proposed approach combines the physical mechanism and the influence of nearest neighbors, and extracts relevant features. The features are then input to construct multiple regression models, which are integrated to obtain the final prognosis. This method was evaluated by the PHM 2016 Data Challenge data sets and the result obtained the best mean squared error score among competitors.
Comparison of Vibration, Sound and Motor Current Signature Analysis for Detection of Gear Box Faults
Authors: Praveenkumar T , Saimurugan M , Ramachandran K I
Published: March 2021

Gear box is used in automobiles and industries for power transmission under different working conditions and applications. Failure in a gear box at unexpected time leads to increase in machine downtime and maintenance cost. In order to overcome these losses, the most effective condition monitoring technique has to be used for early detection of faults. Vibration and sound signal analysis have been used for monitoring the condition of rotating machineries. Motor Current Signature Analysis (MCSA) has rarely been used in gearbox condition monitoring. This work presents a methodology based on vibration, sound and motor current signal analysis for diagnosis of gearbox faults under various simulated gear and bearing fault conditions. Statistical features were extracted from the raw data of these three transducer signals and the best features were selected from the extracted features. Then the selected features were given as an input to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers and their performances were compared. In recent years, Hybrid Electric Vehicles (HEV) are gaining more interest for their advances and this work had a scope in monitoring the power loss in hybrid electric vehicle gearbox using MCSA.
High-Resolution Health Monitoring of Track and Rail Systems with Fiber Optic Sensors and High-Frequency Multiplexed Readouts
Authors: Arakaki Kyle , Raghavan Ajay , Schuh Andreas
Published: March 2021

Health monitoring of railway systems is critical for detecting incipient faults or degradation. In order to reliably do so, an effective monitoring system must be deployed to provide railroad operators with the highest level of operational awareness and safety. In this study, we explore the use of Fiber Bragg Gratings (FBGs) and a highresolution, low-cost optical readout developed at PARC to interrogate the acoustic emissions generated by a train-rail system. The proposed sensing configuration can allow for a scalable, low-cost, field-deployable solution that could enable near real-time monitoring of tracks and wheels. A proof-of-concept was demonstrated with a G-scale train-rail system with FBGs embedded within the ballast layer. Using PARC’s wavelength shift detector, the acoustic emission signal was resolved in both the time and frequency domain. The findings of this work show promise that this could be a viable solution to deploy an optically-based health monitoring system for railroads.
Some influencing factors for passenger train punctuality in Sweden
Authors: Palmqvist Carl-William , O.E. Olsson Nils , Winslott Hiselius Lena
Published: March 2021

Punctuality is regarded as an important measure of the performance of a railway system, and is the one most commonly used and discussed measure both in the industry and among travelers. In many countries, the punctuality of trains, and thus the performance of the railway system, is deemed as lacking. The aim of this article is to study and quantify how several weather-, timetable, operational and infrastructure-related variables influence punctuality in passenger train traffic. This can contribute to better understanding of the performance of railway systems, and help identify possible improvements. The study is based on a dataset containing detailed timetables and records of all 32.4 million train movements for all trains in Sweden during the year of 2015, over 1.1 million departures. Supporting this is a comprehensive register of over 80 000 infrastructure elements, and almost 87 million weather observations. We consider the size and allocation of margins, the existence of negative margins, two measures for traffic volume, the journey time and distance, how often different vehicle individuals are used, the number of line and station interactions between trains, the amount of precipitation, the temperature, wind speed, snow depth, and eight types of infrastructure elements. We show how these variables affect punctuality, and estimate how much of the variation in punctuality can be explained by them. The findings can be used to design timetables, change operational parameters and modify infrastructure design so that punctuality improves. They can also help identify areas which should be prioritized in planning, maintenance and research.
Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles
Authors: F. Hogge Edward , M. Bole Brian , L. Vazquez Sixto , S. Kulkarni Chetan , H. Strom Thomas , L. Hill Boyd , M. Smalling Kyle , C. Quach Cuong
Published: March 2021

This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.
Vibration Analysis Based on HJ-Biplots
Authors: Vairinhos Valter , Parreira Rui , Lampreia Suzana , Lobo Vitor , Galindo Purificación
Published: March 2021

Vibration Analysis (VA) is now routinely used for condition monitoring and failure diagnosis in Condition Based Maintenance (CBM). In the context of VA, a methodology is proposed, based on biplots, to simultaneously display both vibration frequencies and their measurement points, in support of monitoring and diagnostics tasks.In this research, real observational data obtained measuring mechanical vibrations on four generators aboard a Portuguese Navy Ship in real operating conditions is used. A portable vibration collector was employed, and the measurements were taken at 13 measurement points in each one of four generators, using the same collector settings. Spectrograms resulting from vibration measurements were transformed into biplots and used for decision support according to the proposed methodology. Data analysis showed a robust stability in the macrostructure of biplots when observations resulting from different generators of the same model and at the same assumed conditions was analyzed. This invariance allows the specification of reference conditions, rules to detect changes of operating conditions and the emergence of failures. The proposed methodology, once embedded in dedicated software, will reduce the interpretation error in diagnosis and prognosis associated to variability in personnel training and experience. Consequently, it will increase the safe use of VA in an increasing number of situations.
A High Fidelity Model Based Approach to Identify Dynamic Friction in Electromechanical Actuator Ballscrews using Motor Current
Authors: M. Hussain Yameen , Burrow Stephen , Henson Leigh , Keogh Patrick
Published: March 2021

An enhanced model based approach to monitor friction within Electromechanical Actuator (EMA) ballscrews using motor current is presented. The research was motivated by a drive in the aerospace sector to implement EMAs for safety critical applications to achieve a More Electric Aircraft (MEA). Concerns in reliability and mitigating the single of point of failure (ballscrew jamming) have resulted in consideration of Prognostics and Health Monitoring (PHM) techniques to identify the onset of jamming using motor current. A higher fidelity model based approach is generated for a true representation of ballscrew degradation, whereby the motor is modelled using ‘dq axis’ transformation theory to include a better representation of the motor dynamics. The ballscrew kinematics are to include the contact mechanics of the main sources of friction through the Stribeck model. The simulations demonstrated feature extraction of the dynamic behaviour in the system using Iq current signals. These included peak starting current features during acceleration and transient friction variation. The simulated data were processed to analyse peak Iq currents and classified to represent three health states (Healthy, Degrading and Faulty) using k-Nearest Neighbour (k-NN) algorithm. A classification accuracy of ~74% was achieved.
Rethinking Maintenance Terminology for an Industry 4.0 Future
Authors: Hodkiewicz Melinda , Lukens Sarah , Brundage Michael P. , Sexton Thurston
Published: March 2021

Sensors and mathematical models have been used since the 1990’s to assess the health of systems and diagnose anomalous behavior. The advent of the Internet of Things (IoT) increases the range of assets on which data can be collected cost effectively. Cloud-computing and the wider availability of data and models are democratizing the implementation of prognostic health (PHM) technologies. Together, these advancements and other Industry 4.0 developments are creating a paradigm shift in how maintenance work is planned and executed. In this new future, maintenance will be initiated once a potential failure has been detected (using PHM) and thus completed before a functional failure has occurred. Thus corrective work is required since corrective work is defined as “work done to restore the function of an asset after failure or when failure is imminent.” Many metrics for measuring the effectiveness of maintenance work management are grounded in a negative perspective of corrective work and do not clearly capture work arising from condition monitoring and predictive modeling investments. In this paper, we use case studies to demonstrate the need to rethink maintenance terminology. The outcomes of this work include 1) definitions to be used for consistent evaluation of work management performance in an Industry 4.0 future and 2) recommendations to improve detection of work related to PHM activities.
Personal culture dan integritas, mana yang lebih penting untuk mengurangi kecurangan akuntansi?
Authors: Adhivinna Vidya Vitta , Aprilia Irma
Published: March 2021

ABSTRACT: The research aimed to get empirical evidence the influence of internal control, individual morality, personal culture and integrity on accounting fraud at the OPD of Kulon Progo Regency. The sample used was 69 respondent with puropsive sampling method. The data were analized with multiple linear regression. The results of this reserch show that the personal culture has a positive effect on the accounting fraud and  integrity has a negative effect on the accounting fraud, while internal control and has no effect on the accounting fraud.
Radiofrequency Denervation of the Lumbar
Zygapophysial Joints: 10-Year Prospective
Clinical Audit
Authors: Gofeld Michael
Published: March 2021

Background: Evidence for the efficacy of zygapophysial joint nerve radiofrequency neurotomy has remained controversial. Two randomized controlled trials showed positive results, but two others demonstrated no benefit. One carefully performed prospective trial
confirmed high efficacy and lasting pain relief after the procedure; however, selection criteria for this study were superfluous, which resulted in a small number of patients available for follow up.
Objectives: A large clinical audit with routine patient selection and use of appropriate
technique was undertaken to determine the effect of radiofrequency neurotomy of the
lumbar facet joints for relief of chronic low back pain.
Design: Prospective clinical audit for quality was conducted in the pain clinic affiliated with
a tertiary care teaching hospital.
Setting: An interventional pain management setting in Canada.
Methods: All patients with low back pain of more than 6 months’ duration, with or without non-radicular radiation to the buttock, hip, and leg were included.
From January 1991 to December 2000, eligible patients underwent standardized diagnostic work-up, which included a self-reported pain questionnaire, physical examination, review of imaging studies, and diagnostic blockades. Those with an appropriate response to
comparative double diagnostic blocks underwent standardized radiofrequency denervation
of the lumbar zygapophysial joints. Patients were asked to estimate total perceived pain reduction (on a scale from 0% to 100%) at 6 weeks and at 6, 12, and 24 months after the
procedure.
Results: Of the 209 patients, 174 completed the study, and 35 were lost to follow-up or
did not provide complete data for assessment. Of the 174 patients with complete data, 55
(31.6%) experienced no benefit from the procedure. One hundred and nineteen patients
(68.4%) had good (> 50%) to excellent (> 80%) pain relief lasting from 6 to 24 months.
Conclusion: This large, prospective clinical audit indicates that proper patient selection
and anatomically correct radiofrequency denervation of the lumbar zygapophysial joints
provide long-term pain relief in a routine clinical setting.
Key words: low back pain, lumbar zygapophysial joint, radiofrequency denervation, clinical audit
Drug-Related Side Effects of Long-term
Intrathecal Morphine Therapy
Authors: Ruan Xiulu
Published: March 2021

Background: The introduction of intrathecal opioid administration for intractable
chronic non-malignant pain and cancer pain is considered as one of the most important
breakthroughs in pain management. Morphine, the only opioid approved by FDA for intrathecal administration, has been increasingly utilized for this purpose. For over 3 decades, there have been numerous reports on the non-nociceptive side effects associated
with ever increasing long-term intrathecal morphine usage.
Objectives: To review the literature on side effects due to long-term intrathecal morphine therapy with discussions of alternate treatment options.
Design: English-language publications were identified through MEDLINE search and
the bibliographies of identified articles were reviewed.
Results: Most side effects of intrathecal morphine therapy are dose dependent and
mediated by opioid receptors. Common ones include nausea, vomiting, pruritus, urinary
retention, constipation, sexual dysfunction, and edema. Less common ones include respiratory depression, and hyperalgesia. Catheter tip inflammatory mass formation is a
less common complication that may not be mediated by opioid receptors.
Conclusion: The utilization of intrathecal morphine administration for cancer and intractable non-malignant chronic pain represents an important leap forward in pain management. Yet, a wide variety of non-nociceptive side effects may also occur in susceptible patients. The side effects due to intrathecal morphine administration are mostly
mediated by opioid receptors. Treatment usually involves the utilization of opioid receptor antagonist, such as naloxone. Patients considering intrathecal opioid pump therapy
should be informed and advised about the possible side effects associated with longterm intrathecal morphine administration prior to placement of a permanent morphine
infusion pump.
Key words: side effects, intrathecal morphine, opioid receptors
Differentiation of Thoracic Outlet Syndrome
from Treatment-Resistant Cervical Brachial
Pain Syndromes: Development and Utilization
of a Questionnaire, Clinical Examination and
Ultrasound Evaluation
Authors: Jordan Sheldon E
Published: March 2021

Objective: The present study was undertaken to determine which factors differentiate patients with a good outcome after treatment for Thoracic Outlet Syndrome (TOS)
from patients with a poor outcome.
Methods: A total of 85 patients, who were examined during one year, had at least
6 months of follow up after treatment for TOS with either surgery or botulinum chemodenervation.
Results: Socioeconomic factors of work disability or workers’ compensation claims did
not differentiate treatment-responsive TOS from treatment-resistant cases. There was
no difference between the 2 groups regarding the presence of anomalous anatomy detected by ultrasonography or regarding the presence of subclavian artery flow acceleration or occlusion detected by duplex sonography. Several factors were noted more
frequently in treatment-resistant patients: sensory complaints extending beyond lower
trunk dermatomes (42% vs. 10%), weakness extending beyond lower trunk myotomes
(19% vs. 2%), histories of previous non-TOS surgery of the neck or upper limbs (50%
vs.17%), comorbidities of fibromyalgia or complex regional pain syndrome (81% vs.
12%), and depression (35% vs. 10%). Treatment-resistant patients complained about
more widespread functional impairments on a validated Cervical Brachial Symptom
Questionnaire (CBSQ) than treatment-responsive patients. Resistant cases responded
less often to a scalene test block (38% vs. 100%), which is designed to simulate the effects of targeted treatment.
Conclusion: In summary, compared to patients with a good outcome after targeted
treatment, patients with a poor outcome had more diffuse complaints and responded
less often to a scalene test block.
Keywords: Cervical brachial pain, chemodenervation, thoracic outlet syndrome,
ultrasound
Using Peripheral Stimulation to Reduce the
Pain of C2-Mediated Occipital Headaches: A
Preliminary Report
Authors: Melvin, Jr Eugene A.
Published: March 2021

Background: Peripheral nerve stimulation (PNS) is an accepted treatment for neuropathic pain. Recent studies have focused on its potential for relieving headache pain.
Objectives: To investigate the effectiveness of PNS in reducing occipital headache
pain.
Design: A prospective, 12-week pilot study involving 11 patients evaluated before and
after implantation of PNS systems to treat C2-mediated occipital headaches.
Methods: Prior to and at 4 and 12 weeks after implantation, patients completed the
Short-Form McGill Pain Questionnaire (SF-MPQ), Visual Analog Scale (VAS), and Present
Pain Index (PPI). Patients also answered questionnaires and kept diaries to record stimulator use, medication consumption, and numbers of headaches.
Results: A comparison of pre- and post-implantation evaluations showed statistically
significant declines in scores on the SF-MPQ (64%; p = 0.0013), VAS (67%; p < 0.0001),
and PPI (68%; p = 0.0009). Most patients (91% and 64% respectively) reported reductions in medication use and numbers of headaches. Patients also reported a reduction in
headache symptoms and the impact of headaches on activities. Two adverse events were
encountered, one due to a loose connection and, the other caused by lead migration.
Conclusions: PNS reduced headache pain, headache frequency and medication use.
Key words: peripheral nerve stimulation, PNS, peripheral nerve, occipital headache,
headache pain
A Case of Spinal Cord Stimulation in
Raynaud’s Phenomenon: Can Subthreshold
Sensory Stimulation Have an Effect?
Authors: Benyamin Ramsin
Published: March 2021

Spinal cord stimulation is currently used to treat a variety of chronic intractable
painful conditions. We report a case of severe Raynaud’s phenomenon in the
hands refractory to conservative treatment and responsive to diagnostic stellate ganglion block that was effectively treated with a spinal cord stimulator
placed in the cervical epidural space. After capturing the affected areas with
paresthesias, blood flow in the left hand and fingers significantly improved as
evidenced by an increase in skin temperature, a change from cyanotic to pink
appearance and concomitant reduction in pain. Moreover, the patient reported
that limb ischemia and pain could be managed overnight with stimulation intensities that were below sensory perception thresholds. Thus it seems, at least
in the overnight period, paresthesias were not required to maintain pain relief.
This case presents a potential divergence between a requirement for paresthesias and pain relief in spinal cord stimulation therapy for the treatment of Raynaud’s phenomenon. The possible role of the sympathetic nervous system in this
relationship is also discussed.
Key words: spinal cord stimulator, Raynaud’s phenomenon, sensory perception
On the Nature, Assessment, and Treatment of
Fetal Pain: Neurobiological Bases, Pragmatic
Issues, and Ethical Concerns
Authors: Giordano James
Published: March 2021

Over the past 2 decades, the issue of fetal pain and stress has attracted considerable attention and has become the focus of ongoing debate in light of
advances in medical diagnostics and technology that allow invasive prenatal
medical intervention(s), and an enhanced understanding of fetal neurophysiology, together with a broadened appreciation for the relationship of brain
and consciousness. In this essay, we explore the issue of fetal pain based upon
knowledge of both the ontogeny and function of the nervous system and the
interaction of brain, mind, and pain. We posit that contemporary understanding of neurological development fortifies appreciation that pain sensation and
perception occurs during (later) fetal life and that this pain warrants clinical
consideration during the conduct of invasive prenatal procedures. We present
differing perspectives on this issue, discuss the relative merits and difficulties
of these positions, and ultimately describe the fundamental basis for a consideration of fetal pain and address this consideration with regard to pragmatic
and ethical benefits, burdens, and risks. We adopt a neurocentric, yet consilient approach that entails both scientific and philosophical orientations. By attempting to reveal inherent limitations in our contemporary appreciation and
approach(es) to fetal pain, we hope to illustrate the need for and posit potential venues toward resolving these limitations and dilemmas.
Key words: ontogeny, fetal pain, intervention, analgesia, neurobiology, ethics
Comparing Pain Reduction Following
Kyphoplasty and Vertebroplasty for
Osteoporotic Vertebral Compression
Fractures
Authors: Gill J. Brian
Published: March 2021

Background: Osteoporosis with subsequent osteoporotic vertebral compression fractures is an increasingly important disease due not only to its significant economic impact
but also to the increasing age of our population. Pain reduction and stabilization are of
primary importance with osteoporotic vertebral compression fractures. Although many
patients heal with conservative treatment consisting of rest or activity modification, analgesics, and bracing, the management of severe pain compels some patients to seek surgical intervention via 2 procedures: vertebroplasty and kyphoplasty. Although there is
abundant support in the literature for both procedures, there remains debate over which
procedure can most successfully reduce patients’ perception of pain.
Objective: To determine the amount of pain reduction using the visual analog scale
(VAS) with kyphoplasty and vertebroplasty in the treatment of osteoporotic vertebral compression fractures.
Design: Meta-analysis
Methods: A systematic review and meta-analysis of the available literature was performed to quantify the amount of pain reduction using the visual analog scale (VAS) between vertebroplasty and kyphoplasty for osteoporotic vertebral compression fractures.
Results: Twenty-one studies, 14 vertebroplasty and 7 kyphoplasty, qualified for inclusion
representing totals of 1,046 vertebroplasty and 263 kyphoplasty patients treated, respectively. Kyphoplasty and vertebroplasty resulted in a more than 5 point drop in the VAS in the
immediate postoperative period (p<0.00001). Between the two, the difference in early pain
relief was not significant. At final follow-up, the long-term VAS was improved for both procedures, but the final follow-up VAS for vertebroplasty and kyphoplasty was not significant
when compared to the initial postoperative VAS (p=0.25, p=0.38, respectively).
Conclusions: The analysis demonstrates that both procedures reduce the amount of
pain in the immediate postoperative period by approximately 50%. Both procedures reduce pain in symptomatic osteoporotic vertebral compression fractures that have failed
conservative treatment. Randomized controlled trials are needed to provide definitive data
on which procedure is the most effective for vertebral compression fractures.
Key words: vertebroplasty, kyphoplasty, vertebral compression fracture, osteoporosis
Epidural Fibrosis Following Percutaneous Disc
Decompression with Coblation
Technology
Authors: Smuck Matthew
Published: March 2021

Background: Complications reported from percutaneous disc decompression (PDD)
include discitis, anaphylaxis (with chemonucleolysis), instability, increased back pain,
and reherniation. To the best of our knowledge, there is no report of epidural fibrosis
occurring with any of the many types of PDD.
Objective: To document a case of epidural fibrosis following PDD with coblation
technology (Nucleoplasty), a previously unreported complication of this procedure.
Design: Case report.
Methods: Details are presented on a 46-year-old man’s history, diagnostic test results,
treatments, and progression of his symptoms.
Results: Following PDD with coblation technology at L5-S1, the patient noticed improvement in his left lower extremity radicular symptoms and low back pain. He continued to improve over the following week to near complete relief. He resumed his
normal activities. Three months post treatment, he experienced a recurrence of his radicular pain with a diminished left Achilles reflex. A subsequent MRI showed improvement of the previous left paracentral protrusion at L5-S1 along with a new contrast
enhancing soft tissue mass. This mass, consistent with epidural fibrosis, was located
in the left antero-lateral spinal canal and encased the left S1 nerve root. On the patient’s next follow-up visit, he reported spontaneous resolution in his symptoms. He
had stopped all pain medications and returned to his usual activities.
Conclusion: This case is the first reported occurrence of epidural fibrosis following
percutaneous lumbar disc decompression.
Key words: Epidural, fibrosis, nucleoplasty, percutaneous disc decompression.
Ultrasound-guided Suprascapular
Nerve Block Technique
Authors: Harmon Dominic
Published: March 2021

Background: In this article, we describe a case report of using real-time, high-resolution ultrasound guidance to facilitate blockade of the suprascapular nerve. We describe a case report and technique for using a portable ultrasound scanner (38 mm
broadband (13-6 MHz) linear array transducer (SonoSite Micromaxx SonoSite, Inc.
21919 30th Drive SE Bothwell W. A..)) to guide suprascapular nerve block.
Methods: A 44-year old male patient presented with severe, painful osteoarthritis
with adhesive capsulitis of his right shoulder. The ultrasound transducer in a transverse orientation was placed over the scapular spine. Moving the transducer cephalad the suprascapular fossa was identified. While imaging the supraspinatus muscle and the bony fossa underneath, the ultrasound transducer was moved laterally
(maintaining a transverse transducer orientation) to locate the suprascapular notch.
The suprascapular nerve was seen as a round hyperechoic structure at 4 cm depth
beneath the transverse scapular ligament in the scapular notch. The nerve had an approximate diameter of 200 mm. Real-time imaging was used to direct injection in the
scapular notch. Ultrasound scanning confirmed local anesthetic spread.
Results: The patient’s pain intensity decreased. Shoulder movement and function
improved. These improvements were maintained at 12 weeks.
Conclusion: Ultrasound guidance does not expose patients and personnel to radiation. It is also less expensive than other imaging modalities. This technique has applications in both acute and chronic pain management.
Key words: Technique, visualization, real-time, Ultrasound,nerve, analgesia.
Electromyographically Guided Trigger
Point Injections in the Cervicothoracic
Musculature of Obese Patients: A New
and Unreported Technique
Authors: Botwin Kenneth P.
Published: March 2021

Background: Myofascial pain is defined as pain that originates from myofascial
trigger points in skeletal muscle. It is prevalent in regional musculoskeletal pain syndromes, either alone or in combination with other pain generators. The myofascial
pain syndrome is one of the largest groups of under-diagnosed and under-treated
medical problems encountered in clinical practice. Trigger points are commonly seen in
patients with myofascial pain that can be responsible for localized pain in the affected muscles as well as referred pain patterns. Correct needle placement in a myofascial
trigger point is vital to prevent complications and improve efficacy of the trigger point
injection to help reduce or relieve myofascial pain. In the obese patients, these injections may not reach the target tissue. In the cervicothoracic spine, a misguided or misplaced injection can result in a pneumothorax. Here, we review an electromyographically guided trigger point injection technique to avoid this potential pitfall.
Methods: Using a disposable Teflon coated hypodermic injection needle attached to
an electromyography (EMG) machine, a trigger point injection can be performed utilizing electromyographic guidance. This guidance by observing motor unit action potentials (MUAPs) on the EMG screen helps confirm the needle placement to be within the
muscle tissue and not in an adipose tissue or any other non-musculature structure.
Results: The technique is simple when performed by a pain management specialist
who has electromyographic training.
Conclusion: This technique helps confirm proper needle placement within the cervicothoracic musculature in an obese patient in whom the musculature is not readily palpated. This, thus, reduces the potential for a pneumothorax by an improperly
placed injection.
Key words: Trigger point injection, myofascial pain, electromyography
Cadaveric Intervertebral Disc Temperature
Mapping During Disc Biacuplasty
Authors: Pauza Kevin
Published: March 2021

Background: Disc Biacuplasty is a procedure for treating discogenic pain through neuron
ablation by heating intervertebral disc tissue using cooled, bipolar radiofrequency (RF) technology. This study demonstrates temperature profiles created by disc biacuplasty in human
cadavers.
Objective: To assess temperature profiles created by disc biacuplasty in human cadaver
discs.
Design: The design of the experiment is a cadaver study with temperature monitoring in the
intervertebral disc during disc biacuplasty.
Method: Seven human cadaver discs were sectioned from 2 cadavers. Each disc was instrumented with 11 temperature sensors and 2 cooled radiofrequency probes. Correct placement
was verified with the aid of fluoroscopy. The discs were then immersed in a 37°C thermostatic water bath and the treatment protocol was applied. Temperatures were monitored as the
discs were heated.
Results: At 13 minutes, with the settings used in this study, the posterior longitudinal ligament (PLL) temperature reached 40±3°C. The anterior disc reached 41±3°C. The outer layer
of the posterior annulus fibrosus was heated to 54±6°C and the inner two-thirds of the posterior annulus fibrosus reached temperatures of 60±6°C.
Conclusions: The anterior disc and PLL remained at safe temperatures below 45°C while
temperatures throughout the center posterior and posterolateral disc were all raised above
45°C, sufficient for neural ablation.
Key words: denervation, intervertebral disc, discogenic pain, radiofrequency ablation, transdiscal, disc biacuplasty