The Experts below are selected from a list of 41385 Experts worldwide ranked by ideXlab platform
Wei Tian - One of the best experts on this subject based on the ideXlab platform.
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electric signature detection and analysis for power Equipment Failure monitoring in smart grid
IEEE Transactions on Industrial Informatics, 2021Co-Authors: Yan Zhang, Rongbo Zhu, Wei TianAbstract:Power Equipment is one kind of basic element in smart grid, and how to design an efficient detection and analysis scheme of electric signature (ES) for power Equipment Failure (PEF) monitoring is a key and challenging issue. This article proposes an ES detection and analysis method which can monitor multiple kinds of PEF in smart substation. The bottleneck of ES analysis is explored in the view of Heisenberg uncertainty, and an optimal time–frequency analysis method is designed to solve the problems. The proposed method (PM) is based on union of time and frequency bases whose decomposition is realized by Bayesian compressive sensing using Laplace prior. Simulated and field ESs are employed to test PM with comparisons of existing methods. Also, PM is applied in a smart substation of China. Several typical PEFs and measurement soft Failures caused by electromagnetic interference are discussed. The results indicate that the PM can accurately monitor PEFs whose mechanism can be revealed by time–frequency features of ESs, if the required sampling rate and sampling time are satisfied because of its immunity of the uncertainty principle restriction. The robustness in noise environment and optimal time–frequency representation of ESs make the PM an efficient general-purpose PEF monitoring in smart grid by time–frequency analysis.
Igor Svrkota - One of the best experts on this subject based on the ideXlab platform.
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risk assessment model of mining Equipment Failure based on fuzzy logic
Expert Systems With Applications, 2014Co-Authors: Dejan Vlastimir Petrovic, Milos Tanasijevic, Vitomir Milic, Nikola Lilic, Sasa Stojadinovic, Igor SvrkotaAbstract:Abstract The systematic maintenance of mining machinery and Equipment is the crucial factor for the proper functioning of a mine without production process interruption. For high-quality maintenance of the technical systems in mining, it is necessary to conduct a thorough analysis of machinery and accompanying elements in order to determine the critical elements in the system which are prone to Failures. The risk assessment of the Failures of system parts leads to obtaining precise indicators of Failures which are also excellent guidelines for maintenance services. This paper presents a model of the risk assessment of technical systems Failure based on the fuzzy sets theory, fuzzy logic and min–max composition. The risk indicators, severity, occurrence and detectability are analyzed. The risk indicators are given as linguistic variables. The model presented was applied for assessing the risk level of belt conveyor elements Failure which works in severe conditions in a coal mine. Moreover, this paper shows the advantages of this model when compared to a standard procedure of RPN calculating – in the FMEA method of risk assessment.
Wimonrat Sriraj - One of the best experts on this subject based on the ideXlab platform.
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the thai anesthesia incident monitoring study thai aims of anesthetic Equipment Failure malfunction an analysis of 1996 incident reports
Journal of the Medical Association of Thailand Chotmaihet thangphaet, 2009Co-Authors: Chaiyapruk Kusumaphanyo, Somrat Charuluxananan, Aksorn Pulnitiporn, Dujduen Sriramatr, Wimonrat SrirajAbstract:Background: The present study is a part of the multi-centered study of model of anesthesia relating adverse events in Thailand by incident report (The Thai Anesthesia Incident Monitoring Study or Thai AIMS). The objective was to identify the frequency distribution, contributing factors, and factors minimizing incident of Equipment Failure/malfunction. Material and Method: As a prospective descriptive research design, anesthesia providers reported the data as soon as the incidents of Equipment Failure/malfunction occurred. Standardized forms of incident report were then mailed to the center at Chulalongkorn University and three anesthesiologists reviewed the data. Results: Ninety-two cases of Equipment Failure/malfunction were reported from 51 hospitals across Thailand. Between January and June 2007, 92 incidents of Equipment Failure/malfunction were reported out of 1996 anesthesia-related incidents (4.6%). Failed/malfunctioned Equipment included anesthetic circuit (17.4%), anesthesia machine (15.2%), capnography (15.2%), laryngoscope (15.2%), ventilator (12%), pulse oximeter (8.7%), vaporizer (4.3%), endotracheal tube (3.3%), sodalime (3.3%), and electrocardiogram (2.2%). All 16 anesthetic circuit incidents (100%) were detected by clinical signs whereas five incidents (31.3%) were detected firstly by monitors. All 14 laryngoscope malfunction (100%) were detected solely by clinical signs. Only one out of eight (12.5%) of pulse oximeter incidents was detected by clinical signs before the pulse oximeter itself. Three out of four (75%) incidents of vaporizer were detected by clinical signs before monitors. The majority of Equipment malfunction was considered as related to anesthetic (69.6%) and system factors (69.6%) and 71.7% of incidents were preventable. Seventy-four incidents (80.4%) were caused by human error and, specifically, rule-based error in three fourths. Conclusion: Contributing factors were ineffective Equipment, haste, lack of experience, ineffective monitors, and inadequate Equipment. Factors minimizing incidents were Equipment maintenance, pre-use Equipment checking, vigilance, prior experience, and compliance to guidelines. Suggested strategies were quality assurance activity, training, and improvement of supervision. Keywords: Anesthesia, Incidence, Incident report, Complication, Equipment Failure, Human error, System error, Monitoring
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multicentered study of model of anesthesia related adverse events in thailand by incident report the thai anesthesia incident monitoring study methodology
Journal of the Medical Association of Thailand Chotmaihet thangphaet, 2007Co-Authors: Somrat Charuluxananan, Suwanee Suraseranivongse, Prasatnee Jantorn, Wimonrat Sriraj, Thavat Chanchayanon, Surasak Tanudsintum, Chaiyapruk Kusumaphanyo, Thanarat Suratsunya, Surachart Poajanasupawun, Sireeluck KlanarongAbstract:neurosurgical, otorhino-laryngological, urological, and cardiac surgery. Common places where incidents occurred were operating room (61%), ward (10%), and recovery room (9%). Common occurred incidents were arrhythmia needing treatment (25%), desaturation (24%), death within 24hr (20%), cardiac arrest (14%), reintubation (10%), difficult intubation (8%), esophageal intubation (5%), Equipment Failure (5%), and drug error (4%) etc. Monitors that first detected incidents were EKG (46%), Pulse oximeter (34%), noninvasive blood pressure (12%), capnometry (4%), and mean arterial pressure (1%). Conclusion: Common factors related to incidents were inexperience, lack of vigilance, inadequate preanesthetic evaluation, inappropriate decision, emergency condition, haste, inadequate supervision, and ineffective communication. Suggested corrective strategies were quality assurance activity, clinical practice guideline, improvement of supervision, additional training, improvement of communication, and an increase in personnel.
Daniel J Fonseca - One of the best experts on this subject based on the ideXlab platform.
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an expert system for reliability centered maintenance in the chemical industry
Expert Systems With Applications, 2000Co-Authors: Daniel J FonsecaAbstract:Abstract A new framework for the implementation of reliability centered maintenance (RCM) in the initial design phases of industrial chemical processes was developed and implemented. Fuzzy reasoning algorithms were designed to evaluate and assess the likelihood of Equipment Failure mode precipitation and aggravation. Furthermore, an approximate reasoning scheme which considers local, product, and adjacent machinery effects was constructed to prioritize the Equipment Failure modes likely to precipitate in the process. The new RCM approach was implemented through an expert system. The computer system reads the process flowsheet generated by ASPEN Plus and, based on relevant machine operating data, it provides the user with the final process RCM availability structure diagram. This availability diagram consists of a listing of all critical machine Failure modes likely to precipitate, prioritized according to their overall negative impact on the process, as well as important information on their corresponding local and system effects, and suggested controls for their detection.
Sireeluck Klanarong - One of the best experts on this subject based on the ideXlab platform.
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multicentered study of model of anesthesia related adverse events in thailand by incident report the thai anesthesia incident monitoring study methodology
Journal of the Medical Association of Thailand Chotmaihet thangphaet, 2007Co-Authors: Somrat Charuluxananan, Suwanee Suraseranivongse, Prasatnee Jantorn, Wimonrat Sriraj, Thavat Chanchayanon, Surasak Tanudsintum, Chaiyapruk Kusumaphanyo, Thanarat Suratsunya, Surachart Poajanasupawun, Sireeluck KlanarongAbstract:neurosurgical, otorhino-laryngological, urological, and cardiac surgery. Common places where incidents occurred were operating room (61%), ward (10%), and recovery room (9%). Common occurred incidents were arrhythmia needing treatment (25%), desaturation (24%), death within 24hr (20%), cardiac arrest (14%), reintubation (10%), difficult intubation (8%), esophageal intubation (5%), Equipment Failure (5%), and drug error (4%) etc. Monitors that first detected incidents were EKG (46%), Pulse oximeter (34%), noninvasive blood pressure (12%), capnometry (4%), and mean arterial pressure (1%). Conclusion: Common factors related to incidents were inexperience, lack of vigilance, inadequate preanesthetic evaluation, inappropriate decision, emergency condition, haste, inadequate supervision, and ineffective communication. Suggested corrective strategies were quality assurance activity, clinical practice guideline, improvement of supervision, additional training, improvement of communication, and an increase in personnel.
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the thai anesthesia incidents study thai study of anesthetic Equipment Failure malfunction a qualitative analysis for risk factors
Journal of the Medical Association of Thailand Chotmaihet thangphaet, 2005Co-Authors: Sireeluck Klanarong, Waraporn Chauin, Aksorn Pulnitiporn, Wiroj PengpolAbstract:BACKGROUND: Anesthesia Equipment problems may contribute to anesthetic morbidity and mortality. In Thailand, the magnitude and pattern of these problems has not been established. We therefore analyzed the frequency, type and severity of Equipment-related problems, and what additional efforts might be needed to improve safety. MATERIAL AND METHOD: The data were drawn from the Thai Anesthesia Incidents Study (THAI Study) between February 1, 2003 and July 31, 2004 in which anesthesia-related data (i.e. of perioperative problems and their severity) were recorded (by the attending anesthesiologist) from all anesthetic cases on a routine basis. We selected cases under general and regional anesthesia with anesthetic Equipment Failure/malfunction for descriptive analysis. RESULTS: The frequency of anesthetic Equipment problems of the 202,699 recorded cases was approximated 0.04% or 1 : 2252. Two-thirds of the problems (63%) involved the anesthesia machine and of these incidents 73 and 41 percent involved system and human errors, respectively. One patient died and one suffered permanent morbidity. CONCLUSION: The incidence and severity of Equipment problems was low. Aside from improvements to pre-operative Equipment checks, vigilance, continuous quality improvement and quality assurance activities were suggested as strategies to reduce problems.