bluuebunny/crossref_metadata_2025 huggingface dataset:
Fault Diagnosis and Isolation for Brushless Electronic Returnless Fuel Systems
Authors: Hu Yao
Published: February 2021

The brushless electronic returnless fuel system (ERFS) is a complex system, which is subject to different faults such as motor resistance increase and pressure sensor bias. Any of these faults may lead to no-start and safety-related issues such as loss of engine power. It’s challenging to develop a diagnosis and isolation method to detect and isolate faults in the field, with feature signals which capture different fault signatures. In this study, we first develop a set of feature signals which are used to monitor the behavior, track the degradation, predict the potential failures, and diagnose the issues of the brushless ERFS. The feature signals include the estimated resistance, current error ratio, PWM duty cycle error ratio, zero pump speed ratio, etc. Then we develop a fault diagnosis and isolation algorithm to generate the diagnostic results based on feature signals. The algorithm is tested on a vehicle with fault injection. The results show that different faults can be identified correctly with the developed algorithm. This study enables development of diagnosis and prognosis for the brushless ERFS, which can protect customers from walk-home scenarios and safety related issues due to the brushless ERFS failures.
Fault Detection and Isolation for Brake Rotor Thickness Variation
Authors: Du Xinyu , Mai Lichao , Kazemi Hamed , Sadjadi Hossein
Published: February 2021

Brake rotors are critical parts of the disc braking system for modern vehicles. One common failure for brake rotors is the thickness variation, which may result in unpleasant brake pulsation, vehicle vibration during braking, or eventually lead to the malfunction of the braking system. In order to improve customer satisfaction, vehicle serviceability and availability, it is necessary to develop an onboard fault detection and isolation solution. In our previous work, the vibration features of master cylinder pressure, vehicle longitudinal acceleration and wheel speed were identified as fault signatures. Based on these fault signatures, a vibration- based fault detection and isolation algorithm is developed in this work. The difference of frequency response between the braking period and the normal driving period (non-braking) is employed to improve the algorithm robustness. The experiment results demonstrate the proposed algorithm can robustly diagnose the thickness variation fault and isolate the fault to each vehicle corner.
Octave-band Filtering for Convolutional Neural Network-based Diagnostics for Rotating Machinery
Authors: Lee Namkyoung , Azarian Michael , Pecht Michael
Published: February 2021

The performance of a machine learning model depends on the quality of the features used as input to the model. Research into feature extraction methods for convolutional neural network (CNN)-based diagnostics for rotating machinery remains in a developmental stage. In general, the input to CNN-based diagnostics consists of a spectrogram without significant pre-processing. This paper introduces octave-band filtering as a feature extraction method for preprocessing a spectrogram prior to use with CNN. This method is an adaptation of a feature extraction method originally developed for speech recognition. The method developed for diagnosis of machinery faults differs from filtering methods applied to speech recognition in its use of octave bands, to which weighting has been applied that is optimal for machinery diagnosis. Through a case study, the effectiveness of octave-band filtering is demonstrated. The method not only improves the accuracy of the CNN-based diagnostics but also reduces the size of the CNN.
Acoustic emission based condition monitoring study of piston rod seals by varying speed and pressure parameters
Authors: Shanbhag Vignesh Vishnudas , Meyer Thomas , Caspers Leo , Schlanbusch Rune
Published: February 2021

Hydraulic cylinders are used in a wide range of applications such as oil drilling equipment, construction vehicles and manufacturing machines. Seal failure is one of the primitive causes of failure in hydraulic cylinders, possibly leading to fluid spill, unscheduled maintenance, reduced availability and thus leading to lower productivity. Regular visual inspection of seals without affecting the productivity is difficult as the seals are placed internally in the hydraulic cylinder requiring disassembly of the piston. Therefore, condition monitoring is required to assess the current health of the seals. There have been successful attempts made in literature for the assessment of seal quality using acoustic emission-based condition monitoring. However, there have been very few studies performed to diagnose the seal failure under varying speed and pressure parameters. Therefore, this study aims at increasing the understanding of seal failure under varying speed and pressure conditions through correlation with the acoustic emission signal. Experiments were performed on a hydraulic test rig using unworn, semi-worn and worn piston rod seals. For each seal wear condition, experiments were performed for five strokes at pressure conditions of 10, 20, 30 and 40 bar and speeds of 50 mm/s and 100 mm/s.  Continuous acoustic emission data were acquired during all the tests. The acoustic emission signal of each piston rod stroke was analyzed using different acoustic emission features such as power spectral density, root mean square, peak, mean frequency, median frequency and band power. From the acoustic emission analysis, by using power spectral density, mean frequency and median frequency feature it is possible to identify and segregate unworn seal, leakage due to semi-worn seal and leakage due to worn seal in the test rig. The acoustic emission-based condition monitoring methodology developed in this study lays a strong foundation for further research to develop real-time monitoring of the piston rod seal in hydraulic cylinders that are used in the offshore industry.
Hybrid Model for Wind Turbine Main Bearing Fatigue with Uncertainty in Grease Observations
Authors: Yucesan Yigit Anil , Viana Felipe
Published: February 2021

Available historical field data shows that wind turbine main bearing failure can lead to major operation and maintenance costs due to unscheduled downtime. For legacy turbines, fa- tigue is one of the major failure modes and, to a degree, can be partially modeled with physics-based formulations. Unfor- tunately, existing bearing fatigue models can potentially be inaccurate due to lack of understanding of the lubricant degra- dation. One way to enhance these models is to track the grease damage along with the bearing fatigue damage. However, the need of grease degradation data can become an impedi- ment for such strategy. In this paper, we will demonstrate that it is possible to calibrate grease degradation models with cost-efficient periodic visual inspections. Knowing that such inspections introduce observation uncertainty to the model, we will use a hybrid physics-informed deep neural networks to quantify such uncertainties within our models. We built a hybrid model that fuses the physics-based understanding of the bearing fatigue failure with the ability of data-driven layers to compensate the missing physics, with respect to the grease degradation. The proposed hybrid model is also ca- pable of decoding uncertain visual grease inspections with a custom designed classifier. We illustrate the merits of the model with the support of case studies, where we test inspec- tion with different levels of conservatism to train the model and compare the predictions of these models on an artificial wind park. Results from the case studies indicate the success- ful prognostic performance of the trained with limited and noisy observations. While grease damage is predicted with 0.3% root mean square error as a result of baseline inspection campaign, bearing life is prediction is conservatively off only by months for aggressive turbines that have 10 years of life.
Similarity-based anomaly score for fleet-based condition monitoring
Authors: Hendrickx Kilian , Meert Wannes , Cornelis Bram , Gryllias Konstantinos , Davis Jesse
Published: February 2021

An increased number of industrial assets are monitored during their daily use, producing large amounts of data. This data allows us to better monitor the health status of these asset, enabling predictive maintenance to reduce risks and costs caused by unexpected machine failure. Many condition monitoring approaches focus on assessing a machine's health status individually. Often, these approaches require historical data sets or handcrafted fault indicators. However, multiple industrial applications involve monitoring multiple similar operating machines, a fleet. By assuming the healthy behavior for the majority of the machine, deviating signatures can indicate a machine fault.

In this work, we extend our previous proposed framework for fleet-based condition monitoring (Hendrickx et al.). This framework uses interpretable machine learning techniques to automatically evaluate assets within a fleet while incorporating domain knowledge if available. It is designed with four building blocks. In the first block, the user defines a similarity measure to compare machines. This measure can be both data-driven as based on domain knowledge. The second block clusters the machines based on this similarity measure. The third block assesses the health status of a machine by assigning an anomaly score where higher scores represent more deviating behavior. Finally, each of these blocks is visualized in the fourth block to guide a domain expert to set up and gain trust in the framework.

The anomaly score proposed in our previous work has two shortcomings. First, its value can change very abruptly; a slight deviation can cause a machine's anomaly score to change from very low to very high. Second, the score does not accurately represent the anomalousness of a machine. A machine with the highest anomaly score is not necessarily the most deviating. Finally, the anomaly score is assigned to a group of machines. It is thus hard to assess the health status of an individual machine. As a consequence, this anomaly score offers little insights into a machine's performance.

The contribution of this paper is a new implementation of the anomaly score block. Instead of basing our anomaly score on the clustering, we make use of the machine's similarities within the fleet. This solves the shortcomings of the previous anomaly score and defines an individualized, continuous scoring mechanism that represents the anomalousness of a machine.

 

Hendrickx, Kilian, et al. “A General Anomaly Detection Framework for Fleet-Based Condition Monitoring of Machines.” Mechanical Systems and Signal Processing, vol. 139, Elsevier Ltd, 2019, p. 106585, doi:10.1016/j.ymssp.2019.106585.
Domain Adaptation for Structural Health Monitoring
Authors: Ozdagli Ali , Koutsoukos Xenofon
Published: February 2021

In recent years, machine learning (ML) algorithms gained a lot of interest within structural health monitoring (SHM) community. Many of those approaches assume the training and test data come from similar distributions. However, real-world applications, where an ML model is trained on numerical simulation data and tested on experimental data, are deemed to fail in detecting the damage, as both domain data are collected under different conditions and they don’t share the same underlying features. This paper proposes the domain adaptation approach as a solution to particular SHM problems where the classifier has access to the labeled training (source) and unlabeled test (target) domains. The proposed domain adaptation method forms a feature space to match the latent features of both source and target domains. To evaluate the performance of this approach, we present a case study where we train three neural network-based classifiers on a three-story test structure: i) Classifier A uses labeled simulation data from the numerical model of the test structure; ii) Classifier B utilizes labeled experimental data from the test structure; and iii) Classifier C implements domain adaptation by training on labeled simulation data (source) and unlabeled experimental data (target). The performance of each classifier is evaluated by computing the accuracy of the discrimination against labeled experimental data. Overall, the results demonstrate that domain adaption can be regarded as a valid approach for SHM applications where access to labeled experimental data is limited.
Cross-Turbine Training of Convolutional Neural Networks for SCADA-Based Fault Detection in Wind Turbines
Authors: Ulmer Markus , Jarlskog Eskil , Pizza Gianmarco , Goren Huber Lilach
Published: February 2021

Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine.
Development of Multivariate Failure Threshold with Quantifiable Operation Risks in Machine Prognostics
Authors: Jia Xiaodong , Li Wenzhe , Wang Wei , Li Xiang , Lee Jay
Published: February 2021

Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are two major challenges that limit the practicality of these methodologies. 1) How to generate a quantifiable Health Indicator (HI) to represent the operation risks? 2) How to define a reasonable failure threshold to predict RUL? To answer these two questions, this paper proposes a novel methodology for failure threshold determination with quantifiable operation risk in machine prognostics. In the proposed methodology, Fisher distance and Mann-Kendall (MK) test are firstly used to extract useful sensors based on which HI is estimated by applying Principle Component Analysis (PCA). Then, Rao-Blackwellized Particle Filter (RBPF) is employed to obtain the HI prediction and the uncertainties. Afterwards, a Bivariate-Weibull-distribution-based risk quantification model is designed to quantify the cumulative risk over time and over the increase of HI. The failure threshold, which is the ending point of the RUL, varies over different users and applications depending on the level of risk they want to tolerate. The validation of the methodology is based on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The results validate the effectiveness of the proposed risk quantification method and its potential application on machine prognostics.
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control
Authors: Ahmed Ibrahim , Quiñones-Grueiro Marcos , Biswas Gautam
Published: February 2021

Faults are endemic to all systems. Adaptive fault-tolerant control accepts degraded performance under faults in exchange for continued operation. In systems with abrupt faults and strict time constraints, it is imperative for control to adapt fast to system changes. We present a meta-reinforcement learning approach that quickly adapts control policy. The approach builds upon model-agnostic meta learning (MAML). The controller maintains a complement of prior policies learned under system faults. This ``library" is evaluated on a system after a new fault to initialize the new policy. This contrasts with MAML where the controller samples new policies from a distribution of similar systems at each update step to achieve the new policy. Our approach improves sample efficiency of the reinforcement learning process. We evaluate this on a model of fuel tanks under abrupt faults.
Preliminary study towards the definition of a PHM framework for the hydraulic system of a fly-by-wire helicopter
Authors: De Martin Andrea , Jacazio Giovanni , Sorli Massimo
Published: February 2021

On-board hydraulic systems are tasked to provide a number of critical functions to ensure the in-flight operability of rotary-wings vehicles; the hydraulic plant is required to supply power to the flight control actuators and utilities, as well as condition the hydraulic fluid, under a wide range of possible in-service conditions. Being a flight-critical system, the definition of a Prognostics and Health Management framework would provide significant advantages to the users, such as better risk mitigation, improved availability and a reduction in the occurrences of unpredicted failures which still represent one of the more known downsides of helicopters. A preliminary analysis on the effects of the inception and progression of several degradation types is the first step towards assessing if such PHM system is feasible, and which failure modes are more likely to be observed. Moreover, since several key components are frequently provided by different suppliers to the airframer, this preliminary analysis would allow to better assess if an Integrated Vehicle Health Management approach, integrating signals coming from different components, could be beneficial. To pursue this study, a complete model of the hydraulic system for a fly-by-wire helicopter has been prepared. Then, an in-depth simulation campaign was pursued with the aim of studying the interactions between different failure modes, the effects that the propagating degradations have on the system performances and which signals can be used to define a robust set of features. The paper introduces the case-study under analysis, a general configuration for fly-by-wire helicopters, presenting the most prominent peculiarities of the system and the effect of such peculiarities on the definition of health monitoring schemes. The model is then used to describe the behavior of the system under nominal and degraded conditions is introduced. Between the possible failure modes, the interaction between wear in several mechanical components and the occlusion of the hydraulic lines filters was chosen as the subject of this study; motivations are provided and the degradation model described in detail. Hence, results of a wide-ranging simulation campaign are presented, where the time-domain response of the system is used to guide in the definition of a proper set of features able to characterize the selected fault cases. Selected features are presented, chosen according to significant metrics such as correlation with the simulated degradations, signal-to-noise ratio and accuracy. Two different approaches with a varying degree of integration between system signals are proposed and compared. Prognostics is then pursued through well-known particle filter algorithms. The analysis provides promising results on the capability of successfully detecting, isolating and identifying the selected fault mode; laying the foundations for further and more comprehensive studies on the subject.
Prognosis of Wind Turbine Gearbox Bearing Failures using SCADA and Modeled Data
Authors: Desai Arch , Guo Yi , Sheng Shawn , Sheng Shawn , Phillips Caleb , Williams Lindy
Published: February 2021

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.
A Relearning Approach to Reinforcement Learning for control of Smart Buildings
Authors: Naug Avisek , Q'uiñones -Grueiro Marcos , Biswas Gautam
Published: February 2021

This paper demonstrates that continual relearning of control policies using incremental deep reinforcement learning (RL) can improve policy learning for non-stationary processes. This approach has been demonstrated in a data-driven “smart building environment” that we use as a test-bed for developing HVAC controllers for reducing energy consumption of large buildings on our university campus. The non-stationarity in building operations and weather patterns makes it imperative to develop control strategies that are adaptive to changing conditions. On-policy RL algorithms, such as Proximal Policy Optimization (PPO) represent an approach for addressing this non-stationarity, but they cannot be applied to safety-critical systems. As an alternative, we develop an incremental RL technique that simultaneously reduces building energy consumption without sacrificing overall comfort. We compare the performance of our incremental RL controller to that of a static RL controller that does not implement the relearning function. The performance of the static controller diminishes significantly over time, but the relearning controller adjusts to changing conditions while ensuring comfort and optimal energy performance.
Time-of-Failure Probability Mass Function Computation Using the First-Passage-Time Method Applied to Particle Filter-based Prognostics
Authors: Jaramillo Montoya Francisco Roberto , Valderrama Martín , Quintero Vanessa , Pérez Aramis , Orchard Marcos
Published: February 2021

One of the main challenges in prognostics corresponds to the estimation of a system’s probability density function (PDF) for the time-of-failure (ToF) prior to reach a fault condition. An appropriate characterization of the ToF-PDF will let the user know about the remaining useful life of the system or component, allowing the users to prevent catastrophic failures through optimal maintenance schedules. However, the ToF-PDF estimation is not an easy task because it involves both the computation of long-term predictions of a fault indicator of the system and the definition of the hazard zone. In most cases, the trajectory of the fault indicator is assumed as a trajectory with monotonic behavior, and the hazard zone may be considered as a deterministic or probabilistic threshold. This monotonic behavior of the fault indicator enables assuming that the system will only fail once when this indicator reaches the hazard zone, and the ToF-PDF will be estimated according to mathematical definitions proposed in the state-of-the-art. Nevertheless, not all the fault indicators may be considered with a monotonic behavior due to its nature as a stochastic process or regeneration phenomenon, which may entail to errors in the ToF-PDF estimation. To overcome this issue, this paper presents an approach for the estimation of the ToF-PDF using the first-passage-time (FPT) method. This method is focused on the computation of the FPT-PDF when the stochastic process under analysis reaches a specified threshold for the first time only. Accordingly, this work aims to analyze the impact in the estimation of the ToF-PMF (probability mass function) when particle-filter-based prognostics algorithms are used to perform long-term predictions of the fault indicator and compute the probability of failure considering specific hazard zones (which may be characterized by a deterministic value or by a failure likelihood function). A hypothetical self regenerative degradation process is used as a case study to evaluate the performance of the proposed methods.
Immediate and Acute Adverse Effects Following
Transforaminal Epidural Steroid Injections with
Dexamethasone
Authors: Hammam El Abd, MD Omar
Published: February 2021

Background: Transforaminal epidural steroid injections (TFESI) are widely used for the
conservative treatment of radicular pain. The use of dexamethasone in TFESIs is relatively new;
therefore, immediate and acute adverse effects that it may cause are not fully updated.
Objective: To evaluate immediate and acute adverse effects following TFESI with dexamethasone.
Study Design: Prospective, observational study.
Setting: A spine center affiliated with a rehabilitation hospital.
Methods: One hundred fifty consecutive patients receiving TFESI for the management of
radicular and axial spinal pain at the cervical, lumbar, and sacral levels with dexamethasone using
fluoroscopic guidance with digital subtraction technology were enrolled. The occurrence of adverse
effects in patients in the 2-week time period following interventions was monitored through a set
of questionnaires followed up by phone calls scheduled for 1 day, day 3, and day 14. Intensity and
duration of side effects were recorded.
Results: Of the 150 patients enrolled, 31 patients (19.5%) experienced adverse effects within the
first 30 minutes following the intervention. The most common adverse effects were numbness and
tingling in the limb, which developed in 19 patients (11.95%) followed by perineal pruritus that
occurred in 7 cases (4.4%). Patients also reported experiencing adverse effects within the 3 days
following intervention; most complained of headaches, insomnia, hiccups, flushing, and increased
radicular pain. No major complications were noted.
Limitations: The sample size enrolled might be too small to perceive possible rare side effects
related to the procedure. The 2-week follow-up period is a limitation for evaluating late side
effects.
Conclusions: This study offers provision to interventionalists that TFESI with dexamethasone
when performed by experienced hands and with proper technique has minor self-limited transient
adverse effects that can be easily managed. Patients should be made aware of these adverse
effects and their management. Further larger studies are needed to validate the safe use of
dexamethasone and the safety of transforaminal epidural injections.
Key words: Transforaminal epidural steroid injection, complications, dexamethasone
Referred Pain Patterns of the Third and Fourth
Dorsal Interosseous Muscles
Authors: Kang Yoon Kyoo
Published: February 2021

Background: Myofascial pain (MP) is a common disorder that can involve any skeletal muscle
in the human body. There are no published reports of the referred pain patterns of the third and
fourth dorsal interosseous muscles.
Objective: To investigate the referred pain patterns of the third and fouth dorsal interosseous
muscles.
Study Design: Prospective evaluation.
Setting: Academic medical center.
Methods: Twenty healthy adults participated in the study. Needle placement and injection of 0.2
mL 6% hypertonic saline solution into the midpoint of the interosseous muscles were performed
under ultrasonographic (US) guidance. After the injections, the participants were instructed to wait
until they felt the most pain and then draw a pain diagram. This drawing was transferred to the
computer for analysis.
Results: The referred pain distributions for the third dorsal interosseous muscle were as follows:
the interdigital space of the third and fourth fingers, 80%; the distal phalanx of the third and
fourth fingers, 45%; and the ulnar side of the palm, 55%. Three and 6 participants reported pain
on the volar side of the wrist and in the fifth finger, respectively. The referred pain distributions for
the fourth dorsal interosseous muscle were as follows: the interdigital space of the fourth and fifth
fingers, 80%; the hypothenar area, 65%; and the distal phalanx of the fourth and fifth fingers,
60%. Seven and 3 participants also reported pain on the ulnar side of the wrist and the ulnar side
of the forearm, respectively.
Limitation: This study is limited by its small sample size.
Conclusion: Referred pain patterns of the third and fourth interosseous muscles resemble the
pain experienced in C7 or C8 radiculopathies or the ulnar neuropathy. Thus, identification of the
third and fourth interosseous muscle trigger point should be considered when patients experience
pain on the ulnar aspect of the hand and wrist.
Key words: Myofascial pain syndromes, referred pain, dorsal interosseous
Whitacre Needle Reduces the Incidence of
Intravascular Uptake in Lumbar Transforaminal
Epidural Steroid Injections
Authors: Hong JiHee
Published: February 2021

Background: Transforaminal epidural steroid injection (TFESI) is a commonly used interventional
pain management procedures to treat radicular leg pain. Although most reported complications
of TFESI are minor, serious morbidity has also been demonstrated including spinal cord infarction,
paraplegia, and quadriparesis. Suggested mechanisms include direct vascular injury or intravascular
injection of particulate steroid.
Objective: We compared 2 different needle types, Whitacre and Quincke type needles, with
regard to intravascular injection rate with total procedure time and the amount of radiation during
lumbar TFESI.
Study Design: Prospective, randomized trial.
Setting: An interventional pain management practice in South Korea.
Methods: After Institutional Review Board approval, 149 patients undergoing lumbar TFESI
for radicular leg pain were randomly assigned to one of 2 needle groups (Whitacre needle or
Quincke type needle). After final confirmation of intravascular injection with digital subtraction
angiography, total procedure time and amount of radiation exposure during TFESI were measured.
Results: The overall incidence of intravascular injection was 10.4% (28/269). We analyzed the
overall incidence of intravascular injection according to the 2 different needle types. The incidence
of intravascular injection of the Whitacre needle was 5.4% (8/146), whereas the incidence of
intravascular injection of the Quincke needle was 16.2% (20/123). Total procedure time and
amount of radiation required to complete the TFESI in the Whitacre and Quincke needle groups
was 168.4 ± 57.9 (seconds) and 33.4 ± 15.9 (cGy/cm2
), 131.9 ± 46.0 (seconds) and 33.2 ± 15.8
(cGy/cm2
), respectively.
Limitations: The physician who performed the TFESI was not blinded to the type of needle for
detecting intravascular injection. This study was focused on lumbar TFESI, however, most TFESIs are
performed at the L4-5 or L5-S1 level.
Conclusion: The Whitacre needle had the benefit of reducing the incidence of intravascular
injection with minimal differences in technical difficulties and the amount of radiation exposure
during lumbar TFESI.
Key words: Transforaminal epidural steroid injection, complication, intravascular injection,
Whitacre needle, Quincke needle, procedure time, radiation, digital subtraction angiography
Thoracic Epidural Blood Patches in the
Treatment of Spontaneous Intracranial
Hypotension: A Retrospective Case Series
Authors: Feltracco Paolo
Published: February 2021

Background: Spontaneous intracranial hypotension (SIH) results from leaks developing in the
dura mater. The major symptom is orthostatic headache which gradually disappears after lying
down. Lumbar epidural blood patches (EBPs) can be effective in relieving headaches, however,
thoracic and cervical EBPs have also been applied to alleviate the symptoms.
Objective and Methods: Retrospective collection of the main characteristics of SIH, site and
amount of blood injection, and clinical outcomes of 18 patients who underwent thoracic EBPs for
intractable SIH. {
Study Design: Retrospective case series
Results: All thoracic autologous EBPs except 3 were performed in the sitting position. Patients
undergoing epidural puncture at lower thoracic levels (T10-T12) received 25 mL of autologous
blood, 15 mL and 18 mL were injected at spinal segments T5-T7 (mid-thoracic) and T2-T4 (upperthoracic), respectively. Thoracic EBPs did not lead to immediate resolution of symptoms in 3 of 18
patients; one of them underwent early repetition with complete headache relief, one refused a
second EBP, and one experienced partial resolution, followed by a recurrence, and then satisfactory
improvement with a second high thoracic EBP. In long-term follow-up only 2 patients complained
of symptoms or relapses.
Limitations: Retrospective nature of the case series, single center experience.
Conclusions: Performing thoracic-targeted EBPs as the preferred approach theoretically improves
results with respect to those observed with lumbar EBPs. The immediate response was comparable
with that of other reports, but the long-term success rate (90%) turned out to be very effective in
terms of both quality of headache relief and very low incidence of recurrence.
Key words: Central pain, cerebrospinal fluid leak, headache disorders, low intracranial pressure
syndrome, epidural blood patch, occipital headache
Rationale for Prospective Assays of Intrathecal
Mixtures Including Morphine, Ropivacaine and
Ziconotide: Prevention of Adverse Events and
Feasibility in Clinical Practice
Authors: Dupoiron Denis
Published: February 2021

Background: Use of intrathecal admixtures is widespread, but compounding these is sometimes
challenging and may result in errors and complications causing super-potency or sub potency
adverse events in patients or malfunctions in the pump itself.
Objective: The purpose of this study is to evaluate the accuracy of compounding of intrathecal
admixtures through a prospective, systematic quantitative analysis of each component of the
mixture before delivery to patients.
Study Design: Observational follow up prospective study of intrathecal mixtures components
concentrations before refills.
Settings: Assays were performed on all intrathecal admixtures produced by the ICO-Paul Papin
compounding pharmacy between January 2013 and October 2014 using Ultra High Performance
Liquid Chromatography (U.H.P.L.C.). In addition, pH levels of admixtures have been measured
since June 2014. When measured concentrations were 15% above or below the required
concentrations, the mixture was excluded and compounded again.
Results: 1729 mixtures were analyzed. Mean deviation from theoretical values was -1.17%
± 0.28% for morphine, -0.95% ± 1.07% for ropivacaine, and 4.82% ± 0.6% for ziconotide.
Exclusion rates were 8.33% overall, but fell from 11.67% in 2013 to 4.97% in 2014. Most
exclusions were caused by inaccuracy in the dose of ziconotide. Average mixture pH of the 603
tested admixtures was 4.83 ± 0.6%.
Limitations: This study is monocentric and limitations include also its non-randomized nature
with no clinical comparison of the rate of adverse events with a refill process without control of
each component concentrations.
Conclusion: Prospective assays provide benefits in ensuring accuracy of intrathecal mixture
compounding and in preventing overdosing or sub dosing, most notably concerning Ziconotide.
Key words: Intrathecal drug delivery, morphine, ziconotide, ropivacaine, prospective dosages,
adverse events prevention, quality process
Sphenopalatine Ganglion Electrical Nerve
Stimulation Implant for Intractable Facial Pain
Authors: Elahi Foad
Published: February 2021

Persistent idiopathic facial pain can be extremely difficult and significantly challenging
to manage for the patient and the clinician. Pharmacological treatment of these painful
conditions is not always successful.
It has been suggested that the autonomic reflex plays an important role in the pathophysiology
of headaches and facial neuralgia. The key structure in the expression of cranial autonomic
symptoms is the sphenopalatine ganglion (SPG), also known as the pterygopalatine ganglion.
The role of the SPG in the pathophysiology of headaches and facial pain has become clearer
in the past decade.
In this case report, we describe a 30-year-old woman with insidious onset of right facial pain.
She was suffering from daily pain for more than 9 years prior to her visit at the pain clinic.
Her pain was constant with episodic aggravation without a predisposing trigger factor. The
patient was evaluated by multiple specialties and tried multimodal therapy, which included
antiepileptic medications, with minimal pain relief. A SPG block using short-acting local
anesthetic provided significant temporary pain relief. The second and third attempt of SPG
block using different local anesthetic medications demonstrated the same responses.
After a thorough psychological assessment and ruling out the presence of a correctable cause
for the pain, we decided to proceed with SPG electrical neuromodulation. The patient reported
significant pain relief during the electrical nerve stimulation trial. The patient underwent a
permanent implant of the neurostimulation electrode in the SPG region. The patient was
successfully taken off opioid medication and her pain was dramatically responsive during a
6-month follow-up visit. In this article we describe the SPG nerve stimulation and the technical
aspect of pterygopalatine fossa electrode placement.
The pterygoplatine fossa is an easily accessible location. This case report will be encouraging
for physicians treating intractable facial pain by demonstrating a novel therapeutic option.
This report shows a minimally invasive approach to the SPG.
Key words: Sphenopalatine ganglion, persistent idiopathic facial pain, electrical
neuromodulation
Do Acute and Chronic Pain Patients Differ on
Affirmation of One Aspect of Pain Acceptance?
Acknowledgement that a Cure Is Unlikely
Authors: Fishbain David
Published: February 2021

Background: Many chronic pain patients (CPPs) cannot be cured of their pain, but can
learn to manage it. This has led to research on pain “acceptance” which is defined as a
behavior pattern with awareness of pain but not directed at changing pain.
Objective: CPPs who have accepted their pain generally acknowledge that a cure is
unlikely. Time with pain may be necessary to reach such an acknowledgment. It was
therefore hypothesized that fewer acute pain patients (APPs) than CPPs should affirm
that a cure is unlikely and that other described aspects of acceptance such as denial of
disability status should be associated with cure is unlikely in both APPs and CPPs.
Study Design: APPs and CPPs were compared for frequency of endorsement of 2
items/questions with face validity for cure is unlikely: little hope of getting better from
pain (LH) and physical problem (pain) can’t be cured (CBC). Demographic variables and
variables reported associated with acceptance were utilized in logistic prediction models
for the above items in APPs and CPPs.
Setting: Rehabilitation programs/offices.
Results: CPPs were statistically more likely than APPs to affirm both LH and CBC. In both
APPs and CPPs, items reported associated with acceptance, e.g., denial of disability status,
predicted LH and CBC.
Limitations: Information gathered from CPP self-reports.
Conclusions: APPs versus CPPs differ on their affirmation on acknowledgement that a
cure is unlikely.
Key Words: Acceptance, pain acceptance, chronic pain, acute pain, chronic pain
patients, acute pain patients, Battery of Health Improvement (BHI 2), cure disability, illness
uncertainty
Clostridium Sacroiliitis (Gas Gangrene)
Following Sacroiliac Joint Injection – Case
Report and Review of the Literature
Authors: Kurnutala Lakshmi N
Published: February 2021

An 80-year-old woman presented with chronic lumbosacral pain since her laminectomy and
instrumentation 10 years ago. Examination was consistent with left sacroiliitis, and the patient
underwent an elective left sacroiliac joint injection. Two days following her procedure she fell and landed
on her left hip and on the next day, she presented to the emergency room with acutely worsening left
gluteal pain. On evaluation in the emergency department, she was found to be suffering from a fever,
headache, nausea, vomiting, and dysuria. A computed tomography (CT) scan was performed and this
showed extensive foci of gas throughout the posterior aspect of the left iliopsoas muscle, sacrum,
and ileum surrounding the left sacroiliac joint. The patient underwent emergent surgical debridement.
The microbiology report of blood culture revealed clostridium perfringens, while her pathology
showed necrosis and acute inflammation of fibroadipose tissue, skeletal muscle, and fat. The patient’s
hospital course resulted in multi-organ failure and the family elected for comfort care measures only.
Unfortunately, she passed away 36 hours later.
Septic arthritis is a potential catastrophic complication following intra-articular steroid therapy. The
cause of the septic joint can be multifactorial but is likely caused by one of the following processes:
direct inoculation of bacteria by the injection, hematogenous seeding of the percutaneous injection
tract, or due to activation of a quiescent infection by the injected steroid. Clostridial spores are very
resistant to standard aseptic skin preparations, including chlorhexidine and betadine solutions. The only
effective methods to eliminate the spores is to heat them at a temperature greater than 100 degrees
Celsius for 10 minutes or with use of a 10% potassium hydroxide (KOH) solution. We hypothesize
that clostridium spores were present on the patient’s skin from previous stool soiling, and that these
were introduced directly into the soft tissue by needle trauma. Rare complications such as this one are
scarcely reported in the literature and thus it becomes difficult to adequately identify risk factors or to
formulate strategies to improve practice management.
Key words: Lower back pain, sacroiliitis, sacroiliac joint injection, clostridium perfringens, gas
gangrene
Event-Related Cortical Processing in
Neuropathic Pain under Long-Term Spinal
Cord Stimulation
Authors: Krauss Joachim K.
Published: February 2021

Background: Several mechanisms were suggested in the past to explain the beneficial effect
of spinal cord stimulation (SCS) in patients suffering from neuropathic pain. Little is known about
potential supraspinal mechanisms.
Objective: In this study cortical signaling of patients with neuropathic pain and successful longterm treatment with SCS was analyzed.
Study Design: Observational study.
Setting: University hospital, neurosurgical department, outpatient clinic for movement disorders
and pain, institute for cognitive and clinical neuroscience.
Methods: Nine patients with neuropathic pain of a lower extremity with a lasting response to
chronic SCS were included. Cortical activity was analyzed using event-related potentials of the
electroencephalogram after non-painful and painful stimulation. Each patient was tested under
the effect of long-term SCS and 24 hours after cessation of SCS. Cortical areas involved in the
peaks of evoked potentials were localized using a source localization method based on a fixed
dipole model.
Results: Detection threshold and intensity of non-painful stimulation did not differ significantly
on both sides. Pain threshold was significantly lower on the neuropathic side under the effect of
SCS (P = 0.03). Bilateral pain thresholds were significantly lower (P = 0.03 healthy side, P = 0.003
neuropathic side) in 5 patients with increased pain after cessation of SCS.
Under the effect of SCS cortical negativities (N1, N2, N3) and positivities (P1) demonstrated
bilaterally comparable amplitudes. After cessation of SCS, decreased threshold for peripheral
stimulation resulted in lowered negativities on both sides. The positivity P1 was differentially
regulated and was reduced more contralateral to the unaffected side. N2 was localized at the
sensory representation of the leg within the homunculus. The main vector of P1 was localized
within the cingular cortex (CC) and moved more anteriorly under the effect of SCS.
Limitations: The exact time span that SCS continues to have an effect is not known. However,
due to patient discomfort discontinuation of SCS therapy was not prolonged over a 24 hour period.
Further limitations were the low number of patients who agreed to discontinue SCS therapy for
research purposes.
Conclusions: Long-term SCS for treatment of neuropathic pain influenced both pain thresholds
and cortical signalling. Source localization of P1 suggests involvement of regions involved in
cognitive/associative processing of pain.
Key words: Event related cortical processing, neuropathic pain, spinal cord stimulation, SCS