bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of a Data-driven Model for Marine Gas Turbine (MGT) Engine Health Monitoring
Authors: Maraini Daniel , Simpson Mark , Brown Ronald , Porporad Michael
Published: February 2021

Throughout the United States Navy, marine gas turbines (MGT) are used for the production of propulsive and electric power aboard many surface ships. The operational availability of these ships during deployments is contingent on the health and reliability of the installed MGTs. Currently, to ensure the health and proper functionality of these turbines, a series of manual evaluations are employed with varying success (e.g., pre-deployment visual inspections, periodic inspections of predefined wear out modes, characteristic vibration surveys, Integrated Performance Analysis Reports). This paper examines historical records associated with the General Electric LM2500 MGT installed for propulsion aboard Guided Missile Destroyers (DDG) and Guided Missile Cruisers (CG) to develop a deployable model of a healthy engine for the automated, near-real time comparison of sensed data. In traditional gas-path analysis (GPA), parameters such as compressor discharge pressure (CDP), compressor inlet temperature (CIT), and exhaust gas temperature (EGT) are predicted as a function of engine speed using baseline engine data and correction factors. Implementation of GPA in the MGT environment is particularly challenging, as analysis will be limited to narrow operation bands and not account for influence factors such as engine load or the performance of critical subsystems on the engine. In this work, a multi-layer perceptron (MLP) regression model is developed in order to capture the nonlinear relationships between engine controller inputs, external loads, and ambient conditions to selected sensor outputs such as gas generator speed (NGG), low pressure turbine speed (NPT ), and power turbine inlet pressure (P54). Optimal inputs and outputs are chosen using both mutual information (MI) scores and input from subject matter experts. A healthy data set was created using data from 60 MGT’s in service and time-synchronized failure records over a five year period from 2012 to 2017. An inter-engine model was trained from the healthy data set and used to generate model residuals, which show strong correlation with a variety of critical failure modes reported in maintenance history, thus enabling automatic fault detection and remote identification of asset health and reliability.
Developing Health Management Strategies Using Power Constrained Hardware
Authors: Farinholt Kevin M. , Chaudhry Ali , Kim Mark , Thompson Ethan , Hipwell Nathan , Meekins Ryan , Adams Stephen , Beling Peter , Polter Sherwood
Published: February 2021

Condition based maintenance with prognostics (CBM+) is an area of research that interests many in the industrial, energy, and defense sectors. Interest in this concept is focused on lowering overall cost of operations, while also increasing equipment availability and mission readiness. Many applications, however, include power constraints and extensive lifecycle requirements that pose a challenge for existing embedded sensing systems. In some cases these systems can be expected to operate for years to decades without access to wired electricity or reliable energy harvesting sources. In this study a battery powered sensor node is presented that collects operational (pressure, acceleration, position) and environmental (temperature) information to identify and track faults seeded into an instrumented hydraulic test stand. The experimental setup is described in this paper, along with the range of baseline, damage cases, and severities imposed upon the system. Machine learning algorithms are developed specifically to leverage features that can be processed at the sensor node, then applied using low-power, computationally-limited microcontrollers. Several classifiers are considered in this analysis, including random forest and classification trees. The results discussed include prediction accuracies, training and testing requirements, as well as physical power consumption measured using actual hardware. Findings indicate that small sized random forest algorithms (up to 5 trees) can be implemented at the node and provide lower error rates; however they operate with the higher computing times and power requirements when compared to other machine learning techniques. Conversely, classification trees provide a good trade-off in accuracy and computing time, prolonging the operational life of the sensor node given a finite capacity battery as the power source.
Calculating Remaining Useful Life in an Embedded System
Authors: Bechhoefer Eric , Schlanbusch Rune
Published: February 2021

This paper presents a prognostic algorithm with low computational requirements that was implemented on an embedded system. The health of components such as shafts, bearings, and gears, is estimated based on a paradigm where condition indicators (CIs) are mapped into a health indicator (HI) for detecting and identifying faults based on vibration data. For estimating the time evolution of the HI along with remaining useful life (RUL) and its evolution, an alpha-beta and alpha-beta-gamma trackers are prosed. The estimator assumes that the plant noise is converging to a steady state over time. The advantage of the proposed filters is considerably less memory usage and computational load than a Kalman filter. The efficiency of the proposed method is demonstrated with a known fault case based on real-world data. The demonstration shows that the state observer is capable of tracking the evolution of the HI and estimates the RUL with sufficient robustness and performs favorably to previous results based on the Kalman filter. The method is well suited to computing on lower cost smart sensors.
Machine Learning Approach to Diesel Engine Health Prognostics using Engine Controller Data
Authors: Nixon Steve , Weichel Ryan , Reichard Karl , Kozlowski James
Published: February 2021

Many military assets such as surface ships and ground vehicles use diesel engines as their prime movers, and accurately estimating remaining useful life has a high value for enabling predictive maintenance and improving fleet logistics. Most of these diesel engines are already equipped with an array of sensors and digital data busses to support the function of the integrated electronic control module (ECM). There are cost advantages to developing predictive analytics and prognostics using existing embedded sensors. This paper describes a hybrid approach to predictive capabilities that utilizes multiple techniques for the implementation of embedded prognostics using existing sensors. One of the challenges is the fidelity of the data. This paper describes an automated approach to feature and classifier selection for hybrid prognostics. Maintenance records with associated diesel engine sensor data for several different engine classes were acquired, which enabled the training data sets to be organized by failure modes. To help prevent false positives, some filtering of the maintenance logs was required to only include those records likely to be associated with the selected failure mode sensor data sets. The classifier-based, datadriven approach essentially maps multiple channels of the sensor data into subspaces trained to classify multiple distinct failure modes. The intent of this step is to enable fault isolation by quantitatively determining which failure mode class the data best fits statistically. The remaining useful life estimate is provided by tracking the temporal path of the data from the healthy engine classification to one of the known failure mode classes using engine load-hours as the metric for the prognostics.
Recurrent Neural Networks for Online Remaining Useful Life Estimation in Ion Mill Etching System
Authors: TV Vishnu , Gupta Priyanka , Malhotra Pankaj , Vig Lovekesh , Shroff Gautam
Published: February 2021

We describe the approach – submitted as part of the 2018 PHM Data Challenge – for estimating time-to-failure or Remaining Useful Life (RUL) of Ion Mill Etching Systems in an online fashion using data from multiple sensors. RUL estimation from multi-sensor data can be considered as learning a regression function that maps a multivariate time series to a real-valued number, i.e. the RUL. We use a deep Recurrent Neural Network (RNN) to learn the metric regression function from multivariate time series. We highlight practical aspects of the RUL estimation problem in this data challenge such as i) multiple operating conditions, ii) lack of knowledge of exact onset of failure or degradation, iii) different operational behavior across tools in terms of range of values of parameters, etc. We describe our solution in the context of these challenges. Importantly, multiple modes of failure are possible in an ion mill etching system; therefore, it is desirable to estimate the RUL with respect to each of the failure modes. The data challenge considers three such modes of failures and requires estimating RULs with respect to each one, implying learning three metric regression functions - one corresponding to each failure mode. We propose a simple yet effective extension to existing methods of RUL estimation using RNN based regression to learn a single deep RNN model that can simultaneously estimate RULs corresponding to all three failure modes. Our best model is an ensemble of two such RNN models and achieves a score of 1:91 X 10^7 on the final validation set..
Remaining Useful Life Estimation for Systems with Abrupt Failures
Authors: Huang Wei , Khorasgani Hamed , Gupta Chetan , Farahat Ahmed , Zheng Shuai
Published: February 2021

Data-driven Remaining Useful Life (RUL) estimation for systems with abrupt failures is a very challenging problem. In these systems, the degradation starts close to the failure time and accelerates rapidly. Normal data with no sign of degradation can act as noise in the training step, and prevent RUL estimator model from learning the degradation patterns. This can degrade RUL estimation performance significantly. Therefore, it is critical to identify degradation mode during the training step. Moreover, in the application step, predicting RUL when the system is in normal mode and is not showing any sign of degradation can generate inaccurate estimations, and reduce faith in the model. In this paper, we propose a new RUL estimation method that incorporates an early degradation mode detection step to automatically identify the earliest point of time at which the degradation starts to happen. When the degradation mode is detected, a Long Short Term Memory (LSTM) neural network is applied to predict system RUL. As a case study, we apply the proposed method for RUL estimation in 2018 PHM Data Challenge. The case study demonstrates that our solution achieves more accurate RUL estimation compared to several baseline methods.
Probability of Detection (POD)-based Metric for Evaluation of Classifiers Used in Driving Behavior Prediction
Authors: Ameyaw Daniel Adofo , Deng Qi , Söffker Dirk
Published: February 2021

Classiﬁers are functional tools/algorithms that implement classiﬁcations and are widely used in science and technology for state of health estimation, diagnosis systems, and situation/intention recognition of human operators. Certiﬁcation of these classiﬁers plays a crucial role in their selection for a speciﬁc task. Current certiﬁcation approaches utilize the Receiver Operator Curve (ROC) as a standard tool that provides graphically the performance of classiﬁers. Beside the ratio of Detection Rate and False Alarm Rate (combined as ROC), other properties related to process parameters are not considered. In this paper, a new evaluation method based on the Probability of Detection (POD) reliability measure is developed discussing the effect of further process parameters on the classiﬁcation results. Probability of Detection (POD) serves as a performance measure for quantifying the reliability of conventional Nondestructive Testing (NDT) procedures and Structural Health Monitoring (SHM) systems. The approach considers statistical variability of sensor-based measurements. In this publication for the ﬁrst time the signal-response and the binary (hit/miss) approaches are implemented in combination with a process parameter. As example in this publication, the prediction of driving behavior classiﬁcation is used as process parameter. The signal response approach is applied to compare the driving behavior prediction capabilities of Fuzzy Logic-Hidden Markov Model (FL-HMM), Artiﬁcial Neural Network (ANN), and Support Vector Machine (SVM) with respect to the reliability of the prediction for driver behavior related to prediction time. The hit/miss approach is also applied on FL-HMM as example for predicting an upcoming driving maneuver. To account for data uncertainty and variability, conﬁdence bounds are established. A typical and useful criteria for detection at a 90 % probability of detection level with 95 % conﬁdence level is successfully implemented as a new reliability measure and certiﬁcation standard for classiﬁers. In this article a new approach is established permitting a new evaluation approach to classiﬁers. The new approach introduces a POD-based measure for comparison of binary classiﬁers.
Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation
Authors: Beganovic Nejra , Moulik Bedatri , Ali Ahmed Mohamed , S¨offker Dirk
Published: February 2021

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on the them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimalpower flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to batteries lifetime, it becomes unavoidable to develop battery lifetime models, which do not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetimeoriented battery models have to be integrated in power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source is developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance to current operating conditions. Here, the change in charging/ discharging rate (C-rate), overcharging/undercharging of the battery (depth-of-discharge), and the temperature are taken in consideration to define machine model states. The End-of-Lifetime (EoL) is defined as deviation between nominal and current ampere-hour (Ah)-throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serve as a prerequisite forits integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.
Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining
Authors: Hastings Emily , Sexton Thurston , Brundage Michael P. , Hodkiewicz Melinda
Published: February 2021

Maintenance work orders (MWOs) are an integral part of themaintenance workflow. These documents allow techniciansto capture vital aspects of a maintenance job: observed symptoms,potential causes, solutions implemented, etc. TheseMWOs have often been disregarded during analysis becauseof the unstructured nature of the text they contain. However,many research efforts have recently emerged that cleanthese MWOs for analysis. One such effort uses a taggingmethod with an open source toolkit, named Nestor, which relieson experts classifying and annotating the words used inthe MWOs. For example, an expert might classify the words“replace,” “replaced,” and “repalce” as “Solutions” and givethe alias “replace” to all of them. This method greatly reducesthe volume of words used in the MWOs and links words,including misspellings, that have the same or similar meanings.However, one issue with the current iteration of thistool, along with practical usage of data-annotation tools onthe shop-floor more generally, is the usage of only one expertannotator at a time. How do we know that the classificationsof a single annotator are correct, or if it is, for example, feasibleto divide the tagging task among multiple experts? Thispaper examines the agreement behavior of multiple isolatedexperts classifying and annotating MWO data, and providesimplications for implementing this tagging technique for usein authentic contexts. The results described here will help improveMWO classification leading to more accurate analysisof MWOS for decision-making support.
Introducing AnomDB: An Unsupervised Anomaly Detection Method for CNC Machine Control Data
Authors: Zhang Lou , Elghazoly Sarah , Tweedie Brock
Published: February 2021

We propose the application of unsupervised machine learning to automatically detect anomalous behavior preceding machine failure. We achieve this through an approach that utilizes Principal Component Analysis (PCA), latent feature extraction, and Density Based Scanning of Applications with Noise (DBSCAN). We call this method AnomDB. Time series data collected from Computer Numerically Controlled (CNC) machines may benefit from this technique due to its ability to consolidate noisy, multivariate data from CNC controls and detect anomalies without reliance on periodicity of signal. We perform experiments on CNC machine control data to demonstrate the effectiveness of this method in discovering anomalies over other commonly used methods of anomaly detection such as IQR and k-means clustering. We show the effectiveness of this method on a representative example in an actual machine shop, and then on a series of real machining data with synthetic anomalies injected.
Regularized Deep Clustering Method for Fault Trend Analysis
Authors: Qu Yongzhi , Zhang Yue , He David , He Miao , Zhou Dude
Published: February 2021

Effective fault feature extraction is the key of fault diagnosis. In previous works, it is shown that some embedding methods and unsupervised deep learning methods have the ability to extract fault features from raw signals directly, such as PCA and deep autoencoder. Particularly, deep autoencoder has been shown in relevant research that it caneffectively extract the hidden ‘trend’ associated with machinery health states which can be useddirectly for online anomaly detection and prediction. However, in practical online fault diagnosis, the discrimination between successive signals is small due to the slow degradation progress and the external noise. Therefore,it is important to optimize the feature extraction process to achieve better online fault tracking. In this paper, a regularized deep clustering algorithm is proposed to guide the optimization process of feature extraction which combines embedding method and semi-guided learning. A regularization term for the cluster center points is proposed to make the feature optimization convergein a monotonic linear trend. In order to verify the effectiveness of the method, an accelerated gearbox run-to-failure experiment is carried out. The result shows that the feature optimization method can optimize the fault features on the basis of the deep autoencoder algorithm in two aspects: a better distinction of the fault features in short term and a more consistent trend of the gear wear in the long term.
Anomaly Detection and Diagnosis In Manufacturing Systems
Authors: Zope Kalyani , Singh Kuldeep , Nistala Sri Harsha , Basak Arghya , Rathore Pradeep , Runkana Venkataramana
Published: February 2021

Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants. Anomaly detection and diagnosis, that forms an integral part of predictive maintenance, in industrial systems is however challenging due to their complex behavior, interactions among sensors, corrective actions of control systems and variability in anomalous behavior. While several statistical techniques for anomaly detection have been in use for a long time, these are not particularly suited for temporal (or contextual) anomalies that are characteristic of multivariate time series sensor data. On the other hand, several machine learning and deep learning techniques for anomaly detection gained significant interest in the recent years. Further, anomaly diagnosis that involves localization of the faults did not receive much attention. In this work, we compare the anomaly detection and diagnosis capabilities, in semi-supervised mode, of several statistical, machine learning and deep learning techniques on two systems viz. the interacting quadruple tank system and the continuous stirred tank reactor (CSTR) system both of which are representative of the complexity of large industrial systems. The techniques studied include principal component analysis (PCA), Mahalanobis distance (MD), one-class support vector machine (OCSVM), isolation forest, elliptic envelope, dense auto-encoder and long short term memory auto-encoder (LSTM AE). The study revealed that MD and LSTM-AE have the highest anomaly detection capability, followed closely by PCA and OCSVM. The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems.
Data-driven Approach to Equipment Taxonomy Classification
Authors: Saetia Kittipong , Lukens Sarah , Pijcke Erik , Hu Xiaohui
Published: February 2021

A standardized taxonomy enables asset-intensive industrial organizations to systematically measure and track efficiency and performance of assets at different levels in an asset hierarchy. Having a well-structured taxonomy also allows companies to take advantage of emerging data-driven technologies such as PHM through enabling straightforward mapping of assets to analytical content specific to equipment commonalities, e.g., failure modes. However, the complexity and use of equipment taxonomy and coding structures in maintenance management systems vary widely for different organizations. This paper describes a data-driven approach for identifying equipment taxonomy from equipment records in maintenance management systems. The approach combines machine learning-based and rule-based methods into a hybrid man-in-the-loop workflow, which enables rapid and consistent mapping of equipment into a standard taxonomy. A case study is presented to demonstrate the performance and challenges of the proposed approach on equipment taxonomy classification.
Prognostics Framework for Power Semiconductor IGBT Modules through Monitoring of the On-State Voltage
Authors: Degrenne Nicolas , Kawahara Chihiro , Mollov Stefan
Published: February 2021

This paper presents a literature review and an overview of original contributions on diagnostic and prognostic technologies for IGBT power modules based on the monitoring of the on-state voltage (Von). 

First, different kinds of Von sensing circuits are discussed in terms of specifications, implementation, performance and cost. A low-cost and practical circuit is experimentally demonstrated. Then, a method is presented and evaluated for estimating wire-bond degradation. Wire-bond lift-off is the most common wear-out mechanism for industrial power semiconductor IGBT modules. It is effectively detected by measuring Von at the Zero Temperature Coefficient (ZTC) current value. Next, a method is presented to estimate the die temperature. Knowing the die temperature allows estimating the thermo-mechanical stress imposed to the wire-bonds. It is demonstrated to be feasible with ±3°C accuracy/precision using after careful calibration of Von as a Temperature Sensitive Electrical Parameter (TSEP). Finally, an algorithm is presented to process the information generated from the monitoring of Von and estimate the Remaining Useful Life (RUL). It considers Von evolution prior the first wire-bond lift-off and it combines both condition-based and damage accumulation based predictions.

The major contribution of this paper is to present, for the first time, a complete framework for diagnostics and prognostics of power semi-conductor IGBT modules.
Inter-Turn Short-Circuit Failure of PMSM Indicator based on Kalman Filtering in Operational Behavior
Authors: Mansouri Badr , Janati Idrissi Hicham , Venon Audrey
Published: February 2021

In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more and more electric and the aircrafts become lighter in order to save more fuel. Thus, the more electric aircraft will face a great challenge that of the predictive maintenance of its electrical equipments. A key component of these devices is the Permanent Magnet Synchronous Motor (PMSM). In this article we are interested in one of the most recurrent failure of electric motor, that of the inter-turn short circuit failure. The purpose of this study, therefore, is to develop an interturn short-circuit sensitive indicator. It’s based on a linear Kalman filter for a healthy model to estimate residual voltage drops in the rotor reference (d,q). The proposed study shows a high sensitive indicator to the inter-turn short-circuit fault even under external disturbances. As well,  several features can result from it, especially the signal energy, spectral and statistical information, etc. These features can highlight aging laws that will be used as patterns for Prognosis and Health Management (PHM) of inter-turn short-circuit failure.
Improved Fault Detection and Isolation of Small Faults using Multiple Residual Generators and Complex Detection Hypotheses: Case Study of an Electro-Hydraulic Aerospace Actuator
Authors: Mitra Desham , Halder Pulak , Mukhopadhyay Siddhartha
Published: February 2021

This paper presents a study on the performance of Failure Prognosis Systems (FPS) in terms of their Probability of Detection (PD) and Probability of False Alarm (PFA). This is studied through various Receiver Operating Characteristic (ROC) plots for the FPS with residual signals generated by multiple state and parameter estimators and complex detection hypotheses involving the residuals. The study illustrates a methodology for design of FPS in terms of the choice of residual generators, detection hypotheses as well as decision thresholds which optimize PD-PFA performance and also the detection speed. The numerical implementation of the FPS is made for an Electro-Hydraulic Aerospace Actuator.
Performance of Photovoltaic Modules After an Accelerated Thermal Cycling Degradation Test
Authors: Perez Aramis , Marin Luis Gabriel , Fuentes Fernando , Mendoza Patricio , Jimenez Guillermo , Orchard Marcos
Published: February 2021

Typically, datasheets of photovoltaic (PV) modules state that the guaranteed power production remains constant for a certain period of time and after this point, a linear reduction begins reaching an estimated 80% of the original rated power. Moreover, literature reports that the degradation of PV modules reaches less than 1% per year. In this regard, after 20 years of operation a typical PV module will deliver approximately 20% less energy than a the beginning of its life. In this article, the results of an accelerated thermal cycling degradation test are compared to its brand new conditions. These results demonstrate that although the performance among the PV modules is variable when new, after the cycling test the performance of the degraded PV modules is similar. In this case, the power reduction of the degraded module varies from 1.4% up o 7.6% when compared to the initial condition. Furthermore, an Electrochemical Impedance Spectroscopy (EIS) analysis demonstrates that at high frequencies the results are practically the same regardless if the panel is new or degraded, but at low frequencies the variation of the impedance is notorious.
Methodology of Adaptive Prognostics and Health Management Using Streaming Data in Big Data Environment
Authors: Feng Jianshe
Published: February 2021

Prognostics and health management (PHM) has gradually become an essential technique to improve the availability and efficiency of industrial systems. With the rapid advancement of sensor technology and communication technology, a huge amount of real-time data is generated from various applications industry, which brings new challenges to PHM in the context of big data streams. On one hand, high-volume stream data places a heavy demand on data storage, communication, and PHM modeling. On the other hand, continuous fluctuation and drift are essential properties of stream data in an online environment, which requires the PHM model to be capable to capture the new formation in stream data adaptively and continuously. This research proposes a systematic methodology to develop an effective online evolving PHM method with adaptive sampling mechanism against continuous stream data. An adaptive sample selection strategy is developed to effectively select the representative samples in both off-line and online environment. Meanwhile, a probabilistic theory-based modeling approach is developed to update the model with newly selected samples. Finally, the whole methodology is validated with real-world industrial cases. The result comparison between the proposed methodology and state-of-art methods verifies the superiority of the proposed method.
Deep Learning Enable Diagnostics and Prognostics of Machine Health Condition
Authors: Lee Wo Jae , Sutherland John W.
Published: February 2021

This paper presents an overview of the first author’s research being conducted and future research plans for the rest of PhD career. The content of this paper was presented at the PHM 2019 Doctoral Symposium, which was a part of the program at the 11th Annual Conference of the Prognostic and Health Management Society held in Scottsdale, Arizona from September 21-26, 2019. The paper covers the development and application of data-driven approaches to machine health management.
Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction
Authors: Wang Peng (Edward) , Russell Matthew
Published: February 2021

Given its demonstrated ability in analyzing and revealing patterns underlying data, Deep Learning (DL) has been increasingly investigated to complement physics-based models in various aspects of smart manufacturing, such as machine condition monitoring and fault diagnosis, complex manufacturing process modeling, and quality inspection. However, successful implementation of DL techniques relies greatly on the amount, variety, and veracity of data for robust network training. Also, the distributions of data used for network training and application should be identical to avoid the internal covariance shift problem that reduces the network performance applicability. As a promising solution to address these challenges, Transfer Learning (TL) enables DL networks trained on a source domain and task to be applied to a separate target domain and task. This paper presents a domain adversarial TL approach, based upon the concepts of generative adversarial networks. In this method, the optimizer seeks to minimize the loss (i.e., regression or classification accuracy) across the labeled training examples from the source domain while maximizing the loss of the domain classifier across the source and target data sets (i.e., maximizing the similarity of source and target features). The developed domain adversarial TL method has been implemented on a 1-D CNN backbone network and evaluated for prediction of tool wear propagation, using NASA's milling dataset. Performance has been compared to other TL techniques, and the results indicate that domain adversarial TL can successfully allow DL models trained on certain scenarios to be applied to new target tasks.
Deep Learning based Optical Inspection with Centralized Analysis for High Volume Smart Manufacturing
Authors: Li Guoyi , Feng Chao , Woldesenbet Addishiwot , King Bruce , Hadavi Hamid , Moku Vikasini , Loken Kurt , Kunkel Gary
Published: February 2021

Increased capabilities in data storage and exploration provide significant insights for quality assurance in a high volume manufacturing environment. However, these opportunities are associated with great challenges in analytical model development, application deployment, system throughput and reliability. While no commercial software system fully meets the needs of recording head factories in Seagate, a novel strategy named optical inspection with centralized analysis has been developed to detect defects of trailing edges of the recording heads of hard disk drives, and fail the parts when necessary. Leveraging the state-of-the-art artificial intelligence technologies, a deep learning based semantic segmentation engine is built using convolutional neural networks for optical inspection. It has shown an improved accuracy to that of visual inspection performed by human. Meanwhile, a high performance computation engine has been built as a Kubernetes cluster with multiple GPU and CPU units. It is able to achieve the target throughput of three million high-resolution images in each day (i.e., 12 TB image data and 35 images per second). With the high fidelity offered by Kubernetes cluster, the developed applications (inference engine, preprocessor, postprocessor, etc.) serve as containerized microservices independently. Such an architecture ensures the vertical and horizontal scalabilities according to the computation of each individual deployment, while all deployments communicate through an Advanced Message Queuing Protocol cluster without human interference. This analytic framework enables Industry 4.0 recording head manufacturing by integrating advanced AI technologies with a robust edge computation architecture.
Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics
Authors: Liu Chenyu , Mauricio Alexandre , Qi Junyu , Peng Dandan , Gryllias Konstantinos
Published: February 2021

Artificial Intelligence (AI) is escalating in data-driven condition monitoring research. Traditional expert knowledge-based Prognostics and Health Management (PHM) processes can be smartened up with the assistance of various AI techniques, such as deep learning models. On the other hand, current deep learning based prognostics suffers from the data deficit issue, especially considering the varying operating conditions and the degradation modes of the components in practical industrial applications. With the development of simulation techniques, physical-knowledge based digital twin models give engineers access to a large amount of simulation data at a lower cost. These simulation data contain the physical characteristics and the degradation information of the component. In order to accurately predict the Remaining Useful Life (RUL) during the degradation process, in this paper, a bearing digital twin model is constructed based on a phenomenological vibration model. A Domain Adversarial Neural Network (DANN) is used to achieve the domain adaptation target between the simulation and the real data. Regarding the simulation data as the source domain and real data as the target domain, the DANN model is able to predict the RUL without any priori knowledge of the labelling information. Based on real bearing run-to-failure experiments, the performance of the proposed method is validated with high RUL prediction accuracy.
A New Transmucous-Buccal Formulation of
Acetaminophen for Acute Traumatic Pain: A
Non-inferiority, Randomized, Double-Blind,
Clinical Trial
Authors: Pickering Dr Gisèle
Published: February 2021

Background: Acetaminophen (APAP) consumption is large and sometimes excessive, and
guidelines suggest to diminish the dosage prescription. In emergency situations of mild/moderate
pain intravenous (iv) APAP is recommended, but the route of administration is invasive.
Objective: To determine the efficacy of a new transmucous-buccal (B) pharmaceutical form of
125mg-APAP in patients. To confirm the findings obtained in 2 previous clinical trials in healthy
volunteers.
Study Design: A randomized, double-blind, non-inferiority, clinical trial (NCT01586143) was
carried out from 03/05/2012 to 13/05/2013.
Setting: The study took place in the Emergency Department of the University Hospital, ClermontFd, France.
Methods: Forty-three patients were included and 40 analyzed. Patients were eligible if they had
leg or arm traumatic pain of moderate intensity. Pain intensity was measured using a numerical
scale (0 – 10) at regular times for 120 minutes and the main endpoint was at 30 minutes. The
hypothesis of non-inferiority was formulated from previous works with healthy volunteers. After
pain assessment, patients received at baseline 1 g-iv-APAP or saline and concomitantly, 125
mg APAP in 1 mL hydroalcoholic solution (HAS) or placebo (HAS only) was applied in the left
mucogingival sulcus. Non-inferiority of the primary outcome was assessed by one-sided 2 group
t-test of equivalence in means with equal variances with a non-inferiority limit difference of 1.
Other tests were two-sided, with a type I error set at α = 0.05.
Results: Intention-to-treat analysis shows that pain intensity of B-APAP and iv-APAP groups were
not significantly different at t30 minutes (3 ± 1.3 vs 2.7 ± 1.2, P = 0.23, one-sided Student t-test),
and at any other times for 120 minutes. The difference of pain intensity between groups was 0.30
with 2-sided IC90% = [-0.38 – 0.98], not including the non-inferiority margin (∆ = 1). Time to
exhibit a statistical significance in pain relief from baseline was reached at t10 for B-APAP (P = 0.03)
and iv-APAP (P < 0.001). Patients preferred the buccal rather than the iv route of administration.
Limitations: Small population study with limited doses.
Conclusions: For acute traumatic pain of moderate intensity, B-APAP has a non-inferior analgesic
effect compared to iv-APAP for 2 hours. Such a pharmaceutical form would be useful in emergency
situations and breakthrough moderate pain episodes. It would diminish APAP consumption per
dosage unit, limit the risk of adverse events and toxicity, and adhere to actual guidelines of APAP
prescription. It must be now studied in a larger population and with repeated doses.
Key words: Transmucosal delivery, pain, trauma
Intrathecal Baclofen in Spinal Spasticity:
Frequency and Severity of Withdrawal
Syndrome
Authors: Stetkarova Ivana
Published: February 2021

Background: Intrathecal baclofen (ITB) delivered by programmable pump devices represents
an important modality for long-term treatment of severe spinal spasticity.
Objective: One of the serious adverse events is a withdrawal syndrome after sudden
interruption of ITB delivery. In this study, we analyzed the frequency and severity of this
complication. Treatment recommendations follow.
Study Design: Case study
Setting: Academic medical center.
Methods: A total of 54 ITB pumps were successfully implanted in 39 patients with severe
intractable spasticity (24 with spinal cord injury, 15 with multiple sclerosis, 24 men, age range
21 – 59 years).
Results: Eight patients developed a withdrawal syndrome on total a daily dose of ITB
between 90 – 420 µg/day. Seven patients had catheter-related complications. In one patient,
pump failure was observed due to its corrosion. Within the group, baclofen withdrawal
syndrome occurred once in 20.1 pump-years counted out of 160.4 pump-years of ITB
treatment.
Limitations: Small sample size.
Conclusions: ITB withdrawal syndrome is a rare but life-threatening event and prompt
diagnosis before treatment initiation is critical. The reported events were mostly mild due
to the acute treatment regime and probably due to a lower dose of ITB. A prerequisite for
successful ITB treatment is a deep knowledge of complications and their prompt management
in the hands of a multidisciplinary team in specialized centers.
Key words: Spasticity, intrathecal baclofen, pump systems, complications, withdrawal
syndrome
Low Back Pain: Guidelines for the Clinical
Classification of Predominant Neuropathic,
Nociceptive, or Central Sensitization Pain
Authors: Nijs Jo
Published: February 2021

Background: Low back pain (LBP) is a heterogeneous disorder including patients
with dominant nociceptive (e.g., myofascial low back pain), neuropathic (e.g.,
lumbar radiculopathy), and central sensitization pain. In order to select an effective
and preferably also efficient treatment in daily clinical practice, LBP patients should
be classified clinically as either predominantly nociceptive, neuropathic, or central
sensitization pain.
Objective: To explain how clinicians can differentiate between nociceptive,
neuropathic, and central sensitization pain in patients with LBP.
Study Design: Narrative review and expert opinion.
Setting: Universities, university hospitals and private practices.
Methods: Recently, a clinical method for the classification of central sensitization
pain versus neuropathic and nociceptive pain was developed. It is based on a body
of evidence of original research papers and expert opinion of 18 pain experts from 7
different countries. Here we apply this classification algorithm to the LBP population.
Results: The first step implies examining the presence of neuropathic low back
pain. Next, the differential diagnosis between predominant nociceptive and central
sensitization pain is done using a clinical algorithm.
Limitations: The classification criteria are substantiated by several original research
findings including a Delphi survey, a study of a large group of LBP patients, and
validation studies of the Central Sensitization Inventory. Nevertheless, these criteria
require validation in clinical settings.
Conclusion: The pain classification system for LBP should be an addition to available
classification systems and diagnostic procedures for LBP, as it is focussed on pain
mechanisms solely.
Key words: Chronic pain, neuroscience, diagnosis, clinical reasoning, examination,
assessment
Initial Experience with IV Ketamine Infusion for
Treatment of Post Sternotomy Pain in a Patient
with a Total Artificial Heart
Authors: Maher Dermot P.
Published: February 2021

The implantation of total artificial hearts (TAHs) via midline sternotomy for the treatment
of severe biventricular cardiac dysfunction is associated with complex postoperative pain
management. Ketamaine increases blood pressure by raising sympathetic outflow and cardiac
ouput; however, ketamine is a direct vasodilator on isolated arterial tissues. In the setting of
a TAH with a mechanically fixed cardiac output, a ketamine infusion for postoperative pain
control has the potential to decrease blood pressure due to direct arterial vasodilation. We
present the initial experience with a ketamine infusion in a patient with a TAH with minimal
observed decreases in blood pressure and significantly improved postoperative pain.
Key words: Ketamine, postoperative pain, poststernotomy pain, total artificial heart,
mechanical circulation, hypotension, vasodilation
Effects of Stellate Ganglion Block on Breast
Cancer-Related Lymphedema: Comparison of
Various Injectates
Authors: Makkar Jeetinder Kaur
Published: February 2021

Background: Stellate ganglion block (SGB) has been reported to be effective in the treatment of
breast cancer-related lymphedema (BCRL).
Objective: To determine the effects of SGB in BCRL patients and the efficacy of corticosteroids
in SGB.
Study Design: A double-blinded, randomized, controlled trial.
Setting: A single academic hospital, outpatient setting.
Methods: In total, 32 patients with BCRL were recruited. Patients were divided randomly into 3
groups (Group A: 0.5% bupivacaine 5 mL, n = 12; Group B: 0.5% bupivacaine 4.5 mL + 20 mg of
triamcinolone 0.5 mL, n = 10; and Group C: 0.5% bupivacaine 4 mL + 40 mg of triamcinolone 1
mL, n = 10). All patients received 3 consecutive SGBs, every 2 weeks. The primary outcomes were
changes in forearm and upper arm circumference. Circumference was measured at baseline, 2
weeks (before the second injection), 4 weeks (before the third injection), and 8 weeks (one month
follow-up after 3 consecutive SGBs). Moreover, subjective data were collected using EORTC C-30
at baseline and 8 weeks.
Results: After 3 consecutive SGBs, forearm and upper arm circumferences were decreased
significantly from baseline in all groups (P < 0.05/3). The upper arm circumference of group C was
reduced significantly more than that of group A (P < 0.05/3). The subjective data by EORTC-C30
at baseline and one month after 3 consecutive SGBs revealed no statistically significant difference.
Limitations: Relatively few patients were enrolled. We did not compare SGB with any other
BCRL treatment, such as complex decongestive therapy.
Conclusions: This study suggests that SGB may be an effective treatment for BCRL. Furthermore,
it appears that corticosteroids could have an additive effect in SGB.
Key words: Stellate ganglion block, breast cancer, lymphedema:
Pulsed Radiofrequency for Chronic Inguinal
Neuralgia
Authors: Amr Yasser M.
Published: February 2021

Background: Chronic inguinal neuralgia has been reported after inguinal herniorrhaphy,
caesarean section, appendectomy, and trauma to the lower quadrant of the abdomen or inguinal
region.
Objectives: This study was designed to evaluate the efficacy of pulsed radiofrequency in
management of chronic inguinal neuralgia.
Study Design: Randomized, double-blind controlled trial.
Setting: Hospital outpatient setting.
Methods: Twenty-one patients were allocated into 2 groups. Group 1 received 2 cycles of pulsed
radiofrequency (PRF) for each nerve root. In Group 2, after stimulation, we spent the same time
to mimic PRF. Both groups received bupivacaine 0.25% + 4 mg dexamethasone in 2 mL for each
nerve root. Visual Analogue Scale (VAS) was assessed. Duration of the first block effective pain
relief was reported. Repeated PRF blockade was allowed for any patient who reported a VAS > 30
mm in both groups during the one year follow-up period. The number and duration of blocks were
reported and adverse effects were also reported.
Results: Significantly longer duration of pain relief was noticed in Group 1 (P = 0.005) after the
first block, while the durations of pain relief of the second block were comparable (P = 0.59). In
Group 1 the second PRF produced pain relief from the twenty-fourth week until the tenth month
while in Group 2, pain relief was reported from the sixteenth week until the eighth month after
the use of PRF. All patients in Group 2 received 3 blocks (the first was a sham PRF) during the one
year follow-up period. Meanwhile, 2 PRF blocks were sufficient to achieve pain relief for patients
in Group 1 except 4 patients who needed a third PRF block. No adverse events were reported.
Limitations: Small sample size.
Conclusion: For intractable chronic inguinal pain, PRF for the dorsal root ganglion represents a
promising treatment modality.
Key words: Radiofrequency, chronic, inguinal neuralgia