Comprehensive assessment of the effects of operating conditions on membrane intrinsic parameters of forward osmosis (FO) based on principal component analysis (PCA)

2022 ◽  
Vol 641 ◽  
pp. 119909
Author(s):  
Min-kyu Kim ◽  
Ji Woong Chang ◽  
Kiho Park ◽  
Dae Ryook Yang
Keyword(s):  
Principal Component Analysis ◽  
Forward Osmosis ◽  
Principal Component ◽  
Component Analysis ◽  
Comprehensive Assessment ◽  
Operating Conditions ◽  
Intrinsic Parameters

Assessment of Water Quality Using Grey Relational Analysis and Principal Component Analysis

2011 ◽  
Vol 255-260 ◽  
pp. 2829-2835 ◽  
Author(s):  
Yong Qian Cheng ◽  
Hong Mei Ma ◽  
Qian Wu Song ◽  
Yue Zhang
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Grey Relational Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Component Analysis ◽  
Comprehensive Assessment ◽  
Relative Importance ◽  
Relational Analysis ◽  
Grey Relational

This paper investigates the comprehensive assessment of water quality, which is generally a multi-attribute assessment problem. In this context, the grey relational analysis is adopted to settle the no uniformity problem of water quality attributes. The principal component analysis is applied to calculate the weighting values corresponding to various attributes of water quality so that their relative importance can be properly and objectively described. Results of study reveal that grey relational analysis coupled with principal component analysis can effectively solve the multi-attribute water quality assessment. The method is universal and can be a useful tool to improve the comprehensive assessment of water quality.

scholarly journals Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3951
Author(s):  
Eleonora Arena ◽  
Alessandro Corsini ◽  
Roberto Ferulano ◽  
Dario Alfio Iuvara ◽  
Eric Stefan Miele ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Monte Carlo ◽  
Anomaly Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Operating Conditions ◽  
Production Plant ◽  
Use Case ◽  
Model Errors ◽  
Unseen Data

This paper investigates a use case of robust anomaly detection applied to the scenario of a photovoltaic production factory—namely, Enel Green Power’s 3SUN solar cell production plant in Catania, Italy—by considering a Monte Carlo based pre-processing technique as a valid alternative to other typically used methods. In particular, the proposed method exhibits the following advantages: (i) Outlier replacement, by contrast with traditional methods which are limited to outlier detection only, and (ii) the preservation of temporal locality with respect to the training dataset. After pre-processing, the authors trained an anomaly detection model based on principal component analysis and defined a suitable key performance indicator for each sensor in the production line based on the model errors. In this way, by running the algorithm on unseen data streams, it is possible to isolate anomalous conditions by monitoring the above-mentioned indicators and virtually trigger an alarm when exceeding a reference threshold. The proposed approach was tested on both standard operating conditions and an anomalous scenario. With respect to the considered use case, it successfully anticipated a fault in the equipment with an advance of almost two weeks, but also demonstrated its robustness to false alarms during normal conditions.

Key Technologies for Health Management and Applications of Aero-Engine Based on Principal Component Analysis

2014 ◽  
Vol 538 ◽  
pp. 218-221
Author(s):  
Jian Guo Cui ◽  
Xu Zhao ◽  
Jun Li ◽  
Bo Cui ◽  
Li Ying Jiang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Health Management ◽  
Good Condition ◽  
Health Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
Operating Conditions ◽  
Analysis Model ◽  
Aero Engine

Aero-Engine health management generally involves a series of activities over the period from the aerospace breaking down until it returning to normal, including signal processing, monitoring, health assessment, decision supporting, human-computer interaction, and so on. As one of the key technology of Aero-Engine health management, fault diagnosis plays a very important role on the safe operation of Aero-Engine. Currently, for effective challenging Aero-Engine health management, a fault diagnosis of Aero-Engine based on Principal Component Analysis (PCA) s proposed. Firstly, based on a variety of significant parameters of the collected information, principal component analysis model is established. Secondly, the fault diagnosis of engine operating conditions is realized by comparing the T2 statistic and Squared Prediction Error (SPE) statistic as an engine running in good condition threshold limits. Finally, through the variable's cumulative contributions diagram with the behavior of SPE overrun, the fault variables are effectively worked out. Experimental results show that the proposed PCA method can efficiently come true Aero-Engine health management o and has some engineering applications values.

Optimization of Comprehensive Assessment Parameters System for Equipment Maintenance Quality Based on Principal Component Analysis

2011 ◽  
Vol 328-330 ◽  
pp. 2343-2347
Author(s):  
Qing Xia Geng ◽  
Sheng Feng Wang ◽  
Hui Min Zhang ◽  
Jian Zhang ◽  
Fan Qi Meng
Keyword(s):  
Principal Component Analysis ◽  
Quality Management ◽  
Statistical Data ◽  
Principal Component ◽  
Component Analysis ◽  
Comprehensive Assessment ◽  
Equipment Maintenance ◽  
Important Process ◽  
Analysis Method ◽  
Optimization Of Parameters

Testing and optimization of assessment parameters is important process for constructing general, perfect assessment parameters systems. It is emphasis and difficulty for research on parameters system for comprehensive assessment of maintenance quality to test and optimize parameters of comprehensive assessment of maintenance quality. The paper tests and optimizes parameters system for assessment of references [4] by principal component analysis method based on existing statistical data of equipment maintenance of quality management. An instructive research has done on optimization of parameters system for comprehensive assessment of maintenance quality.

scholarly journals Fault Detection using Principal Component Analysis and Mean Value Modeling in a 2 MW gas engine

Respuestas ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 15-24
Author(s):  
Yulineth Cárdenas-Escorcia ◽  
Guillermo Valencia-Ochoa ◽  
Juan Campos-Avella
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Value ◽  
Operating Conditions ◽  
Gas Engine ◽  
Operation Conditions ◽  
Abnormal Operation

This paper describes the combination of statistical techniques and mathematical modeling in order to developed a fault detection system in a 2 MW natural gas engine under actual operation conditions. The Mixing chamber, turbochargers, intake and exhaust manifolds, cylinders, throttle and bypass valves, and the electric generator, which are the main components of the gas engine, were studied under a mean value engine to complement the statistical analysis. Objective: The main objective of this paper is to integrate two approaches in order to relate the faults with the changes of mean thermodynamic values of the system, helping to sustain the engine in optimal operating conditions in terms of reliability. The Principal Component Analysis (PCA), a multivariate statistical fault detection technique, was used to analyze the historical data from the gas engine to detect abnormal operation conditions, by means of statistical measures such as Square Prediction Error (SPE) and T2. These abnormal operation conditions are categorized using cluster techniques and contributions plots, to later examine its causes with the support of the results of a mean value mathematical model proposed for the system. The integration of the proposed methods allowed successfully identify which component or components of the engine might be malfunctioning. Once combined, these two methods were able to accurately predict and identify faults as well as shut downs of the gas engine during a month of operation. Statistical analysis was used to detect faults on a 2 MW industrial gas engine, also the result were compared with a mean value model in order to detect variations of the thermodynamic properties of the system at abnormal conditions.

Principal Component Analysis Based Gear Fault Diagnostics in Different Stages of a Multi-Stage Gearbox Subjected to Extensive Fluctuating Speeds

2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Vamsi Inturi ◽  
G. R. Sabareesh ◽  
K. Supradeepan ◽  
P. K. Penumakala
Keyword(s):  
Principal Component Analysis ◽  
Feature Vector ◽  
Principal Component ◽  
Component Analysis ◽  
Operating Conditions ◽  
Discrete Wavelet ◽  
Acoustic Feature ◽  
Acoustic Signatures ◽  
Multi Stage ◽  
Gear Fault

Abstract Multi-stage gearboxes are vulnerable to failures often due to the extreme operating conditions, which may result in long downtimes. The current investigation is intended to examine the fault diagnostic capabilities of the integrated vibro-acoustic condition monitoring scheme while diagnosing the local/lumped defects exist at different speed stages of a multi-stage gearbox subjected to fluctuating/varying speeds. Experiments are performed, and the raw vibration and acoustic signatures are acquired simultaneously from the three-stage spur gearbox. Later, the raw data signatures are processed individually through discrete wavelet transform, and various descriptive statistics are extracted. Further, feature-level fusion is executed to obtain the integrated vibro-acoustic feature vector set for various speed stages of the gearbox. Finally, the obtained integrated feature vector set is classified using principal component analysis (PCA). It is observed that PCA performed using the integrated vibro-acoustic scheme clearly distinguishes among the various damage severity levels of pinion tooth exist at different speed stages of the gearbox.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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