bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fuzzy-FMEA Risk Assessment Approach for Offshore Wind Turbines
Authors: Dinmohammadi F. , Shafiee M.
Published: March 2021

Failure Mode and Effects Analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for risk and reliability analysis. However, several limitations are associated with its implementation in offshore wind farms: (i) the failure data gathered from SCADA system is often missing or unreliable, and hence, the assessment information of the three risk factors (i.e., severity, occurrence, and fault detection) are mainly based on experts’ knowledge; (ii) it is rather difficult for experts to precisely evaluate the risk factors; (iii) the relative importance among the risk factors is not taken into consideration, and hence, the results may not necessarily represent the true risk priorities; and etc. To overcome these drawbacks and improve the effectiveness of the traditional FMEA, we develop a fuzzy-FMEA approach for risk and failure mode analysis in offshore wind turbine systems. The information obtained from the experts is expressed using fuzzy linguistics terms, and a grey theory analysis is proposed to incorporate the relative importance of the risk factors into the determination of risk priority of failure modes. The proposed approach is applied to an offshore wind turbine system with sixteen mechanical, electrical and auxiliary assemblies, and the results are compared with the traditional FMEA.
The New Second and Higher Order Spectral Technique for Damage Monitoring of Structures and Machinery
Authors: Gelman Len
Published: March 2021

The new second and higher order spectral technique, the cross-covariance of complex spectral components, is proposed for monitoring damage of structure and machinery Normalization of the proposed technique is also developed. It is shown by simulation that the proposed technique provides effectiveness gain for detecting of damage compared to the higher order spectra.
Autonomous Vehicle Battery State-of-Charge Prognostics Enhanced Mission Planning
Authors: Zhang Bin , Tang Liang , DeCastro Jonathan , Roemer Michael , Goebel Kai
Published: March 2021

Most mission planning algorithms are designed for healthy systems. When faults occur in a system, it is advantageous to optimize the mission plan by taking the system health condition into consideration. In this paper, a mission planning scheme is proposed to integrate real-time prognostics in a receding horizon path planning framework to accommodate the system fault. In this scheme, the state-of-charge of a battery is monitored and predicted by a particle-filtering based prognostic algorithm. The predicted state-of-charge and remaining useful life of the battery are used in the mission planning to minimize mission failure risk. A series of experiments are presented on a robotic platform, which is powered by a Lithium-ion battery, to demonstrate and verify the proposed scheme.
An Effective Predictive Maintenance Approach based on Historical Maintenance Data using a Probabilistic Risk Assessment: PHM14 Data Challenge
Authors: Mohammad Rezvanizaniani Seyed , Dempsey Jacob , Lee Jay
Published: March 2021

This paper presents an effective health assessment and predictive maintenance technique for industrial assets. The technique and algorithms applied to data sets provided by the Prognostics and Health Management Society 2014 Data Challenge. The data contains usage and part consumption for three years. In short, the usage data contains a parameter that roughly measures asset usage, and the part consumption data includes information regarding part replacement and maintenance actions. The first two years of data are considered as "Training" data and the third year is considered as "Testing" data. The proposed method built on the probability of the failure risk during training dataset. The main objective is to develop a model based on first two years data set (training) and determine the high risk and low risk times of failure for each individual asset for the third year.Training data shows many maintenance activities with 14 different codes. The principle difficulty is to detect the Preventive Maintenance (PM) in the training data. The paper presents the method in three main steps: the first step is to recognize the PM pattern based on time and type of maintenance activity via the training data. The second step is to determine the high-risk time intervals based on PM times by checking the frequency of the failures at specific times between each PM. The third step is to predict the high risk time intervals in the testing data using the information acquired from the training data. The score predicted by this probabilistic risk assessment method won the first place in the PHM Data Challenge Competition.
Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator
Authors: Gopinath R. , Kumar C. Santhosh , Vishnuprasad K. , Ramachandran K. I.
Published: March 2021

Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline systemis not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively.
Prognostic and Health Management of Active Assets in Nuclear Power Plants
Authors: Agarwal Vivek , Lybeck Nancy , Pham Binh T. , Rusaw Richard , Bickford Randall
Published: March 2021

This paper presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and two wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.
Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems
Authors: Kim Hyunjae , Moon Ha Jong , Park Jungho , Kim Sunuwe , Kim Keunsu , Chan Jang Beom , Oh Hyunseok , D. Youn Byeng
Published: March 2021

In the 2015 PHM Data Challenge Competition, the goal of the competition problem was to diagnose failure of industrial plant systems using incomplete data. The available data consisted of sensor measurements, control reference signals, and fault logs. A detailed description of the plant system of interest was not revealed, and partial fault logs were eliminated from the dataset. This paper presents a fault log recovery method using a machine-learning-based fault classification approach for failure diagnosis. For optimal performance, it was critical to be able to utilize a set of incomplete data and to select relevant features. First, physical interpretation of the given data was performed to select proper features for a fault classifier. Second, Fisher discriminant analysis (FDA) was employed to minimize the effect of outliers in the incomplete data sets. Finally, the type of the missing fault logs and the duration of the corresponding faults were recovered. The proposed approach, based on the use of an incomplete-data-trained FDA classifier, led to the second-highest score in the 2015 PHM Data Challenge Competition.
PHM Based Adaptive Power Management System for a More Electric Aircraft
Authors: K.Sebastian Robin , Peripinayagam Suresh , Alghassi Alireza
Published: March 2021

This research work presents a novel approach that addresses the concept of an adaptive power management system design and development framed in the Prognostics and Health Monitoring(PHM) perspective of an Electrical power Generation and distribution system(EPGS).PHM algorithms were developed to detect the health status of EPGS components which can accurately predict the failures and also able to calculate the Remaining Useful Life(RUL), and in many cases reconfigure for the identified system and subsystem faults. By introducing these approach on Electrical power Management system controller, we are gaining a few minutes lead time to failures with an accurate prediction horizon on critical systems and subsystems components that may introduce catastrophic secondary damages including loss of aircraft. The warning time on critical components and related system reconfiguration must permits safe return to landing as the minimum criteria and would enhance safety. A distributed architecture has been developed for the dynamic power management for electrical distribution system by which all the electrically supplied loads can be effectively controlled. The different failure modes were generated by injecting faults into the electrical power system using a fault injection mechanism. The data captured during these studies have been recorded to form a “Failure Database” for electrical system. A hardware in loop experimental study was carried out to validate the power management algorithm with FPGA-DSP controller. In order to meet the reliability requirements a Tri-redundant electrical power management system based on DSP and FPGA has been developed.
Defining Optimal Maintenance Scope for Multiple k-out-of-n Load-Sharing Production Systems Connected in Series Based on RUL Predictions
Authors: R. Rodrigues Leonardo
Published: March 2021

This paper presents a method to define the optimal maintenance scope of a production system consisting of multiple k-out-of-n systems connected in series. Maintenance recommendations are based on Remaining Useful Life (RUL) predictions obtained from a Prognostics and Health Management (PHM) system for each production unit within the production system. Defining the techniques applied in order to estimate the degradation level of production units is out of the scope of this paper. It is assumed here that a PHM system is available and provides the degradation level and RUL estimates for each production unit. The goal is to find the maintenance scope that minimizes the expected total cost per cycle until the next maintenance activity. A k-out-of-n load-sharing system is assumed, which means that the failure of a production unit results in a higher load (and consequently a higher degradation rate) on the surviving production units. The total cost comprises the production cost and the maintenance cost. Production cost of each k-out-of-n system is also affected by the number of surviving production units. A preventive maintenance cost is incurred to maintain a degraded but still functional production unit. A corrective maintenance cost is incurredto maintain a failed production unit. An Ant Colony Optimization (ACO) approach is adopted, which allows the proposed method to deal with large instances of the problem. A numerical example is presented to illustrate the application of the proposed method.
An Approach To Mode and Anomaly Detection with Spacecraft Telemetry Data
Authors: Biswas Gautam , Khorasgani Hamed , Stanje Gerald , Dubey Abhishek , Deb Somnath , Ghoshal Sudipto
Published: March 2021

This paper discusses a mixed method that combines unsupervised learning methods and human expert input for analyzing telemetry data from long-duration robotic space missions. Our goal is to develop more automated methods for detecting anomalies in time series data. Once anomalies are identified using unsupervised learning methods we use feature selection methods followed by expert input to derive the knowledge required for building on-line detectors. These detectors can be used in later phases of the current mission and in future missions for improving operations and overall safety of the mission. Whereas the primary focus in this paper is on developing data-driven anomaly detection methods, we also present a computational platform for data mining and analytics that can operate on historical data offline, as well as incoming telemetry data on-line.
Railcar Diagnostics Using Minimal-Redundancy Maximum- Relevance Feature Selection and Support Vector Machine Classification
Authors: Shahidi Parham , Maraini Daniel , Hopkins Brad
Published: March 2021

Railcar condition is an important factor in the complex web of relationships between railroads, railcar leasing companies, shippers and railcar builders. The most important reasons for this are operational safety and economic considerations pertaining to equipment maintenance. In this study, an approach is presented for the diagnostics of railcar component health from vibration data, utilizing mutual information (MI) based minimal-redundancy-maximalrelevance (mRMR) feature selection and multi-class support vector machine classification. The proposed monitoring solution is a data-driven method which was developed with measurements taken at a railroad test laboratory under controlled conditions. Vibration data was collected from multiple locations on a railcar over several test runs, each utilizing wheelsets with different levels of wear. The input of controlled wheel wear levels was aimed at varying the system outputs to resemble those of cars with different levels of mileage in revenue service. The measured data sets were processed in the time domain, frequency domain and throughwavelet transforms, resulting in the extraction of a set of 687 features from the acceleration signals. A maximum-relevance minimum-redundancy feature selection algorithm was usedto find the optimal combination of features for classification. The algorithm performance was tested for the effect of feature set size, different kernels and scaling techniques on classification accuracy. The results and methods of this assessment are presented in the paper. The paper concludes with a proposal for a monitoring strategy aimed at specifically detecting faulty components and practicing predictive maintenance.
A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement
Authors: Tong Xiaorui , D. Ardakani Hossein , Siegel David , Gamel Ellen , Lee Jay
Published: March 2021

Data-driven modeling and fault detection of multi-stage manufacturing processes remain challenging due to the increasing complexity of the manufacturing process, the lack of structural data, data multi-dimensionality, and the additional difficulty when dealing with large data sets. The implementation of add-on sensors and establishing data acquisition, transfer, storage and analysis has the potential to facilitate advanced data modeling techniques. However, besides the associated costs, dealing with high-volume multi-dimensional data sets can be a major challenge. This paper presents a novel methodology for early fault identification of multi-stage manufacturing processes using a statistical approach. The major advantage of the proposed methodology is its reliance on only the product quality measurements and basic product manufacturing records, given the presence of peer sets. This leads to a feasible faultidentification solution in a sensor-less environment without investing costly data collection systems. The developed methodology transforms the end-of-process quality measurements to a process performance metric based on a density-based statistical approach and a peer-to-peer comparison of the machines at one stage of the process. This approach allows one to be more proactive and identify the problematic machines that could be affecting product quality. A case study in an actual multi-stage manufacturing process is used to demonstrate the effectiveness of the developed methodology.
Experimental Validation of Model-Based Prognostics for Pneumatic Valves
Authors: S. Kulkarni Chetan , J. Daigle Matthew , Gorospe George , Goebel Kai
Published: March 2021

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run-to-failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valvesin the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.
A Bi-Level Weibull Model with Applications to Two Ordered Events
Authors: Song Shuguang , Liu Hanlin , Zhang Mimi , Xie Min
Published: March 2021

In this paper, we propose and study a new bivariate Weibull model, called Bi-levelWeibullModel, which arises when one failure occurs after the other. Under some specific regularity conditions, the reliability function of the second event can be above the reliability function of the first event, and is always above the reliability function of the transformed first event, which is a univariate Weibull random variable. This model is motivated by a common physical feature that arises fromseveral real applications. The two marginal distributions are a Weibull distribution and a generalized three-parameter Weibull mixture distribution. Some useful properties of the model are derived, and we also present the maximum likelihood estimation method. A real example is provided to illustrate the application of the model.
A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis
Authors: E. Warner James , F. Bomarito Geoffrey , D. Hochhalter Jacob , P. Leser William , E. Leser Patrick , A. Newman John
Published: March 2021

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model.
International Journal of Prognostics and Health Management, ISSN 2153-2648, 2017 019 1 A Condition Based Maintenance Implementation for an Automated People Mover Gearbox
Authors: Ashasi-Sorkhabi Ali , Fong Stanley , Prakash Guru , Narasimhan Sriram
Published: March 2021

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.
Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes
Authors: Lee Wan-Jui
Published: March 2021

Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding. However, air leakage is difficult to be detected via visual inspection and therefore most air leakage defects are run to fail. In this research we present a framework that not only can detect air leakages but also predicts the severity of air leakages so that action plans can be determined based on the severity level. The proposed contextual anomaly detection method detects air leakages based on the on/off logs of a compressor. Air leakage causes failure in the context when the compressor idle time is short than the compressor run time, that is, the speed of air consumption is faster than air generation. In our method the logistic regression classifier is adopted to model two different classes of compressor behavior for each train separately. The logistic regression classifier defines the boundary separating the two classes under normal situations and models the distribution of the compressor idle time and run time separately using logistic functions. The air leakage anomaly is further detected in the context that when a compressor idle time is erroneously classified as a compressor run time. To distinguish anomalies from outliers and detect anomalies based on the severity degree, a density-based clustering method with a dynamic density threshold is developed for anomaly detection. The results have demonstrated that most air leakages can be detected one to four weeks before the braking failure and therefore can be prevented in time. Most importantly, the contextual anomaly detection method can pre-filter anomaly candidates and therefore avoid generating false alarms. To facilitate the decisionmaking process, the logistic function built on the compressor run time is further used together with the duration of an air leakage to model the severity of the air leakage. By building the prediction model on the severity, the remaining useful life of the air braking pipe until it reaches a certain level of severity can be estimated.
Integrated Health Monitoring for the actuation system of high-speed tilting trains
Authors: De Martin Andrea , Dellacasa Andrea , Jacazio Giovanni , Sorli Massimo
Published: March 2021

Tilting trains are designed to reach high speed on pre-existing railroads without the need of adjusting the tracks geometry or building dedicated lines; the tilting of the carbody keeps an acceptable level of comfort by limiting the lateral acceleration felt by passengers when the train runs along curved tracks with speed higher than the balance speed built into the curve geometry. As such, they are often used to reduce travel times on routes with several curves. Tilting is performed through a position-controlled actuation system which operates according to the commands received from the train control system: in the studied configuration, the torque needed to tilt the car body with respect to the bogie is provided by a series of hydraulic actuators, while the position information used to close the control loop comes from two capacitive sensors located in the front and rear part of each vehicle. Tilt angle measurement is vital for the system operation and for ensuring a safe ride; the traditional solution in case of discrepancy between the signals of the two tilt angle sensors of any vehicle is to disable the tilting function while limiting the train speed to avoid issues during changes of direction. In a similar fashion, the failure in one (or more) of the tilting actuators would result in the loss of the tilting capability and the return to a fixed configuration operating at reduced speed. It should be noticed that the negative impact of the loss of the tilting system is not limited to the faulty train, since it might affect the entire traffic schedule on the interested lines. The paper presents an integrated Health Monitoring framework that makes intelligent use of all available information thus enhancing the system availability, allowing its operation even in presence of faulty sensors and detecting the onset of failures in the actuation system. At the same time its use can facilitate maintenance organization, simplify the spare parts logistics and provide help to the traffic management. The proposed framework has been developed taking advantage of a high-fidelity model of the physical system validated through comparison with experimental mission profiles on the Lichtenfels - Saalfeld and Battipaglia - Reggio Calabria routes, which have been used by the train manufacturer to assess the performance of their tilting trains.
A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms
Authors: Cannarile Francesco , Baraldi Piero , Colombo Pierluigi , Zio Enrico
Published: March 2021

Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.
Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
Authors: Huang Tingting , Liu Chao , Sharma Anuj , Sarkar Soumik
Published: March 2021

Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner.
Enhanced production surveillance using probabilistic dynamic models
Authors: Tewari Ashutosh , De Waele Stijn , Subrahmanya Niranjan
Published: March 2021

Production surveillance is the task of monitoring oil and gas production from every well in a hydrocarbon field. A key opportunity in this domain is to improve the accuracy of flow measurements per phase (oil, water, gas) from a multi-phase flow. Multi-phase flow sensors are costly and therefore not instrumented for every production well. Instead, several low fidelity surrogate measurements are performed that capture different aspects of the flow. These measurements are then reconciled to obtain per-phase rate estimates. Current practicesmay not appropriately account for the production dynamics and the sensor issues, thus, fall far short in terms of achieving a desired surveillance accuracy. To improve surveillance accuracy, we pose rate reconciliation as a state estimation problem. We begin with hypothesizing a model that describes the dynamics of production rates and their relationship with thefield measurements. The model appropriately accounts for the uncertainties in field conditions and measurements. We then develop robust probabilistic estimators for reconciliationto yield the production estimates and the uncertainties therein. We highlight recent advancements in the area of probabilistic programming that can go a long way in improving the performance and the portability of such estimators. The exposition of our methods is accompanied by experiments in a simulation environment to illustrate improved surveillance accuracy achieved in different production scenarios.
Gas Turbine Engine Condition Monitoring Using Gaussian Mixture and Hidden Markov Models
Authors: R. Jacobs William , L. Edwards Huw , Li Ping , Kadirkamanathan Visakan , Mills Andrew R.
Published: March 2021

This paper investigates the problem of condition monitoring of complex dynamic systems, specifically the detection, localisation and quantification of transient faults. A data driven approach is developed for fault detection where the multidimensional data sequence is viewed as a stochastic process whose behaviour can be described by a hidden Markov model with two hidden states — i.e. ‘healthy / nominal’ and ‘unhealthy / faulty’. The fault detection is performed by first clustering in a multidimensional data space to define normal operating behaviour using a Gaussian-Uniform mixture model. The health status of the system at each data point is then determined by evaluating the posterior probabilities of the hidden states of a hidden Markov model. This allows the temporal relationship between sequential data points to be incorporated into the fault detection scheme. The proposed scheme is robust to noise and requires minimal tuning. A real-world case study is performed based on the detection of transient faults in the variable stator vane actuator of a gas turbine engine to demonstrate the successful application of the scheme. The results are used to demonstrate the generation of simple and easily interpretable analytics that can be used to monitor the evolution of the fault across time.
Coal Pulverizer Prognostics Data Challenge in PHMAP 2017 and Suggestions for Future Studies
Authors: Oh Hyunseok , Ho Seo Yun , Mo Min-Hwan , Kim Joo-Hyung
Published: March 2021

Pulverizers in a power plant are used to grind coal into the form of a fine powder for combustion in a power plant. To secure reliable operation, redundant pulverizers should be installed in power plants and monitored. Pulverizers can be operated and maintained in a cost-effective manner by correctly estimating the current health condition and remaining useful life of the pulverizer’s gearbox system. To this end, the Data Challenge Committee of the PHM Asian Pacific 2017 (PHMAP 2017) conference organized an open competition on the topic of coal pulverizer health estimation based on a real working power station. This paper presents the original problem and given facts, as well as the list of winners of the Data Challenge Competition. We anticipate that this paper can be used as a reference in the development of a prognostic method that can accurately predict the health conditions of coal pulverizers.
Guidelines for the Characterization of the Internal Impedance of Lithium-Ion Batteries in PHM Algorithms
Authors: Pérez Aramis , Benavides Matias , Rozas Heraldo , Seria Sebastián , Orchard Marcos
Published: March 2021

This article aims to describe the most important aspects to consider when using the concept of internal impedance in algorithms that focus on characterizing the degradation of lithium-ion (Li-ion) batteries. The first part of the article provides a literature review that will help the reader understand the concept of electrochemical impedance spectroscopy (EIS) and how Li-ion batteries can be represented through electrochemical or empirical models, in order to interpret the outcome of typical discharge and/or degradation tests on Li-ion batteries. The second part of the manuscript shows the obtained results of an accelerated degradation experiment performed under controlled conditions on a Li-ion cell. Results show that changes observed on the EIS test can be linked to battery degradation. This knowledge may be of great value when implementing algorithms aimed to predict the End-of-Life (EoL) of the battery in terms of temperature, voltage, and discharge current measurements. The purpose of this article is to introduce the reader to several types of Li-ion battery models, and show how the internal impedance of a Li-ion battery is a dynamic parameter that depends on different factors; and then, illustrate how the EIS can be used to obtain an equivalent circuit model and how the different electronic components vary with the use given to the battery.
Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data Based on Ensemble Linear Regression and Paris’s Law
Authors: Rao Meng , Yang Xingkai , Wei Dongdong , Chen Yuejian , Meng Lijun , Zuo Ming J.
Published: March 2021

This paper presents methods for the 2019 PHM Conference Data Challenge developed by the team named "Angler". This Challenge aims to estimate the fatigue crack length of a type of aluminum structure using ultrasonic signals at the current load cycle and to predict the crack length at multiple future load cycles (multiple-step-ahead prediction) as accurately as possible. For estimating crack length, four crack-sensitive features are extracted from ultrasonic signals, namely, the first peak value, root mean square value, logarithm of kurtosis, and correlation coefficient. An ensemble linear regression model is presented to map these features and their second-order interactions with the crack length. The Best Subset Selection method is employed to select the optimal features. For predicting crack length, variations of the Paris’ law are derived to describe the relationships between the crack length and the number of load cycles. The material parameters and stress range of Paris’ law are learned using the Genetic Algorithm. These parameters will be updated based on the previous-step predicted crack length. After that, the crack length corresponding to a future load cycle number for either the constant amplitude load case or variable amplitude load case is predicted. The presented methods achieved a score of 16.14 based on the score-calculation rule provided by the Data Challenge committees, and was ranked third best among all participating teams.
Prognosis of a Degradable Hydraulic System
Authors: El Adraoui Imad , Gziri Hassan , Mousrij Ahmed
Published: March 2021

This article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a model based on learning  by multilayer perception (MLP). The purpose of this paper is to provide a modeling approach that makes it possible to determine the level of degradation reached by the system at any given point of time, in a precise way. Thus, the horizon of the failure will be produced with a minimum error compared to the discrete jump model used in the literature. The proposed approach consists of using a neural network with fewer layers and optimal computing time. We performed data learning (tests) in order to illustrate a regression of good correlation of these data (tests) on a centrifugal pump with satisfactory performance parameters and compared it with other commonly used methods.
Peran citra merek hijau, niat merek untuk meningkatkan loyalitas pembelian
Authors: Rahayu Sri , Nanik Rin , Sulastri Susi
Published: March 2021

This study aims to determine the role of green brand image in an effort to increase brand intention, and purchase loyalty. Research locations were held in various universities in the province of D.I.Yogyakarta. The population of this research is university students, and the research sample is 222 students. The sampling technique uses purposive sampling technique, minimum age 18, and a maximum 25 years old, laptop users at least 1 year. Data were collected through a survey by distributing questionnaires. The research data is processed using structural equation modeling, AMOS 21 program. The results of this study indicate that green brand image has a positive and significant effect on brand intention, and purchase loyalty. The more the green brand image increases, the more brand intention and purchase loyalty increases. This study also has limitations, and includes suggestions for future research.
Opioid Complications and Side Effects
Authors: Benyamin Ramsin
Published: March 2021

Medications which bind to opioid receptors are increasingly being prescribed
for the treatment of multiple and diverse chronic painful conditions. Their use
for acute pain or terminal pain is well accepted. Their role in the long-term
treatment of chronic noncancer pain is, however, controversial for many reasons. One of the primary reasons is the well-known phenomenon of psychological addiction that can occur with the use of these medications. Abuse and
diversion of these medications is a growing problem as the availability of these
medications increases and this public health issue confounds their clinical utility. Also, the extent of their efficacy in the treatment of pain when utilized on a
chronic basis has not been definitively proven. Lastly, the role of opioids in the
treatment of chronic pain is also influenced by the fact that these potent analgesics are associated with a significant number of side effects and complications. It is these phenomena that are the focus of this review.
Common side effects of opioid administration include sedation, dizziness,
nausea, vomiting, constipation, physical dependence, tolerance, and respiratory depression. Physical dependence and addiction are clinical concerns that
may prevent proper prescribing and in turn inadequate pain management.
Less common side effects may include delayed gastric emptying, hyperalgesia, immunologic and hormonal dysfunction, muscle rigidity, and myoclonus.
The most common side effects of opioid usage are constipation (which has a
very high incidence) and nausea. These 2 side effects can be difficult to manage and frequently tolerance to them does not develop; this is especially true
for constipation. They may be severe enough to require opioid discontinuation,
and contribute to under-dosing and inadequate analgesia. Several clinical trials are underway to identify adjunct therapies that may mitigate these side effects. Switching opioids and/or routes of administration may also provide benefits for patients. Proper patient screening, education, and preemptive treatment
of potential side effects may aid in maximizing effectiveness while reducing the
severity of side effects and adverse events. Opioids can be considered broad
spectrum analgesic agents, affecting a wide number of organ systems and influencing a large number of body functions.
Key words: Opioids, morphine, methadone, fentsnyl, oxycodone, hydrocodone, xymorphone, codeine, adverse events, narcotics, side effects, constipation, sedation, hearing loss, tolerance, addiction, hyperalgesia
Editoriale
Authors: Serena Tiziana
Published: March 2021

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Fotografie e istituzioni museali: il sistema della doppia copia e l’accumulo dei fondi. Le Regie Gallerie di Firenze, 1860-1906
Authors: Binazzi Marta
Published: March 2021

Beginning in the late 1850s, photographic companies reproducing artworks kept in the Royal Galleries of Florence were required to submit copies of their work to the Ministry of Public Education and the museum’s director. This paper shows that while photographers obeyed the rule, their photographs were stored away and left mostly unused for over two decades before the process of creating a proper collection began in the 1880s. Countering traditional histories of how photographic collections were constituted, this paper analyzes bureaucratic practices of documentation and questions the status of photographs in 19th century museums.
La fotografia nel museo d’arte a fine Ottocento: sovrapposizioni e occasioni per una rinnovata filologia visiva. Alcuni spunti
Authors: Levi Donata
Published: March 2021

The article deals with the effects of photography on the practices of art historians, focusing on some German cases. Photography, defined by Paul de Saint-Victor in 1887 as the “musée en action de l’art européen,” offered unprecedented opportunities for a renewed visual philology. Yet this evolution was more complex and less unidirectional than is generally thought. The illustrated publications of Gustav Scheuer in the 1860s attest to different ways of manipulating photographic images and illuminate the connections between photography and comparative methods, which initially concerned the relationships between original paintings and photographs much more than those among paintings themselves.
Il patrimonio fotografico Alinari: excursus storico e questioni attuali
Authors: Tomassini Luigi
Published: March 2021

Following the purchase by the Regione Toscana (2019), various possibilities arise for the reorganization and cultural destination of the Alinari photographic heritage. The essay outlines a historical excursus intended to illustrate the methods, phases and type of materials with which such an important archival-museum complex has been formed over time, always playing, in different ways, a particularly important role in Italian and international visual culture.
“Alle Hilfsmittel an der Hand”: note sulle prime fotografie collezionate da Aby Warburg
Authors: Mazzucco Katia
Published: March 2021

There is no evidence of a consistent theoretical position of Aby Warburg regarding photography, but his scattered notes on the subject allow for a deductive evaluation. The considerable use of photographs that he made in his work suggests a wide range of methodological approaches, with significant implications for the disciplinary and methodological definition of art history and the development of photographic documentation at the turn of the 20th century. This essay provides examples of Warburg’s early attention to photography, both as a research tool and a required piece of equipment for any research institute.
Los frutos de la educación
Authors: Guzmán Díaz César Ariel , Casasola Santisteban Antonio José , Mansilla Arroyo Cindy Violeta , Sosa Guerra Yadira Fernanda
Published: March 2021

La búsqueda de contar con una educación real y que aporte al crecimiento de la humanidad, incluidos todos nosotros, nos lleva a identificar elementos esenciales que nos permita transcender en las nuevas generaciones y en nosotros como hombros de las nuevas generaciones. Los saberes necesarios que debemos compartir se abordarán, considerando conocimientos pertinentes, sin considerar tener la verdad absoluta, todo esto con el fin de ir generando frutos en la educación, entre ellos el evitar los determinismos, mantener la curiosidad promoviendo el tejer juntos el conocimiento de la condición humana y la aventura en la que nos encontramos como humanidad.
Meningkatkan Karakter Bangunan Bersejarah & Kenyamanan Visual dengan Pencahayaan Buatan Studi Kasus: Gedung London Sumatra Indonesia di Kota Medan
Authors: Harry Wibowo
Published: March 2021

Kota Medan terkenal dengan banyaknya bangunan bersejarah yang tersebar di seluruh penjuru kota. Bangunan itu memiliki desain arsitektur kolonial yang megah. Karakter bangunan dan detail arsitektural yang unik menjadikan bangunan ini berbeda dari yang lain. Tapi sangat disayangkan bangunan-bangunan tersebut tidak dapat dinikmati keindahannya pada saat malam hari. Kurangnya pencahayaan buatan yang diterima oleh bangunan tersebut. Penelitian ini bertujuan untuk memperoleh gambaran kenyamanan visual masyarakat khususnya pada saat malam hari. Metode yang digunakan adalah metode kualitatif untuk dapat memahami fenomena yang dialami oleh subjek penelitian seperti persepsi dan dengan cara deskripsi dalam bentuk kata-kata dan bahasa, pada suatu konteks khusus yang alamiah. Persepsi mengenai kualitas pencahayaan bangunan sangat ditentukan oleh kesan yang keluar pada diri seseorang pada saat melihatnya, baik secara langsung maupun tidak langsung (menggunakan media gambar atau layar komputer). Memudahkan deskripsi visual pencahayaan bangunan pada malam hari, maka dilakukan pemodelan dengan software Enscape. Hasil dari penelitian ini diperoleh bahwa kualitas visual bangunan pada malam hari dengan konsep perencanaan buatan yang baik (simulasi komputer) mampu meningkatkan karakteristik bangunan tersebut, dibandingkan dengan menggunakan pencahayaan seadanya. Kualitas visual bangunan yang baik maka akan meningkatkan berbagai aktifitas masyarakat disekitarnya dan pada akhirnya akan menjadikan bangunan bersejarah tersebut menjadi spot kawasan pada malam hari.