Estimation and Analysis of SRGM with Multiple Change Points and Bath-Shaped Failure Detection Rate

Operations with Unmanned Aerial Vehicles (UAVs) require reliability to execute missions. With the correct diagnostic, it is possible to predict vehicle failure during or before the flight. The objective of this work is to present a testing tool, which analyzes and evaluates drones during the flight in indoor environments. For this purpose, the framework Ptolemy II was extended for communication with real drones using the High-Level Architecture (HLA) for data exchanging and synchronization. The presented testing environment is extendable for other testing routines and is ready for integration with other simulation and analysis tools. In this paper, two failure detection experiments were performed, with a total of 20 flights for each one, which 80\% were used to train a decision tree algorithm, and the other 20% flights to test the algorithm in which one of the propellers had an anomaly. The failure rate or detection rate was 70\% for the first experiment and 90% for the second one.

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Link Failure Detection and Classification in Wireless Sensor Networks using Classification Method

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3886.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1132-1135

Keyword(s):

System Performance ◽

Detection Rate ◽

Sensor Node ◽

Weak Link ◽

Failure Detection ◽

Sensor Nodes ◽

Wireless Sensor ◽

Delivery Ratio ◽

Link Failure ◽

Packet Delivery

This paper develops a method to detect the failures of wireless links between one sensor nodes to another sensor node in WSN environment. Every node in WSN has certain properties which may vary time to time based on its ability to transfer or receive the packets on it. This property or features are obtained from every node and they are classified using Neural Networks (NN) classifier with predetermined feature set which are belonging to both weak link and good link between nodes in wireless networks. The proposed system performance is analyzed by computing Packet Delivery Ratio (PDR), Link Failure Detection Rate (LFDR) and latency report.

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Developing a 3D decision-making grid based on failure modes and effects analysis with a case study in the steel industry

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-03-2019-0096 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Arash Shahin ◽

Ashraf Labib ◽

Ali Haj Shirmohammadi ◽

Hadi Balouei Jamkhaneh

Keyword(s):

Decision Making ◽

Condition Monitoring ◽

Detection Rate ◽

Failure Modes ◽

Failure Detection ◽

Operating Conditions ◽

Content Type ◽

Casting Unit ◽

Third Dimension

PurposeThe aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.Design/methodology/approachIn a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.FindingsFindings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.Research limitations/implicationsIn failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.Originality/valueIn conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

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