scholarly journals Evaluating Unsupervised Fault Detection in Self-Healing Systems Using Stochastic Primitives

2015 ◽  
Vol 1 (1) ◽  
pp. e3 ◽  
Author(s):  
Chris Schneider ◽  
Adam Barker ◽  
Simon Dobson
Keyword(s):  
Fault Detection ◽  
Self Healing

scholarly journals Performance evaluation for self-healing distributed services and fault detection mechanisms

2006 ◽  
Vol 72 (7) ◽  
pp. 1172-1182 ◽  
Author(s):  
E. Grishikashvili Pereira ◽  
R. Pereira ◽  
A. Taleb-Bendiab
Keyword(s):  
Performance Evaluation ◽  
Fault Detection ◽  
Self Healing ◽  
Distributed Services ◽  
Detection Mechanisms

LEARNING PROCESS BEHAVIOR FOR FAULT DETECTION

2011 ◽  
Vol 20 (05) ◽  
pp. 969-980 ◽  
Author(s):  
CÁSSIO M. M. PEREIRA ◽  
RODRIGO F. DE MELLO
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Linear Models ◽  
Moving Average ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Self Healing ◽  
Detection Techniques ◽  
Process Behavior ◽  
Changes Over Time

Recently, there has been an increased interest in self-healing systems. These types of systems are able to cope with failures in the environment they execute and work continuously by taking proactive actions to correct these problems. The detection of faults plays a prominent role in self-healing systems, as faults are the original causes of failures. Fault detection techniques proposed in the literature have been based on three mainstream approaches: process heartbeats, statistical analysis and machine learning. However, these approaches present limitations. Heartbeat-based techniques only detect failures, not faults. Statistical approaches generally assume linear models. Most machine learning techniques assume the data is independent and identically distributed. In order to overcome all these limitations we propose a new approach to address fault detection, which also gives insight into how process behavior changes over time in the presence of faults. Experiments show that the proposed approach achieves a twofold increase in F -measure when compared to Support Vector Machines (SVM) and Auto-Regressive Integrated Moving Average (ARIMA).

scholarly journals Towards fault detection and self-healing of chemical processes over wireless sensor networks

2020 ◽  
pp. 9-14
Author(s):  
B. Dorneanu ◽  
H. Arellano-Garcia ◽  
H. Ruan ◽  
A. Mohamed ◽  
P. Xiao ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Chemical Processes ◽  
Wireless Sensor ◽  
Self Healing

Hierarchical Fault Detection and Recovery Framework For Self-Healing WSN

2021 ◽  
Author(s):  
R. Anitha ◽  
Tapas Bapu B R ◽  
V. Nagaraju ◽  
Pradeep. S
Keyword(s):  
Fault Detection ◽  
Pso Algorithm ◽  
Delivery Ratio ◽  
Detection Accuracy ◽  
Fault Recovery ◽  
Self Healing ◽  
Round Trip ◽  
Packet Delivery ◽  
Network Process ◽  
Round Trip Delay

Abstract Wireless Sensor Network (WSN) contains several sensor nodules that are linked to each other wirelessly. Errors in WSN may perhaps be because of several causes which bring about hardware damage, power thwarts, incorrect sensor impression, faulty communication, sensor deficiencies, etc. This damages the network process. In this paper, we propose to develop a Hierarchical Fault Detection and Recovery Framework (HDFR) for Self-Healing WSN. This framework consists of three modules: Fault detection, fault confirmation and fault recovery. In fault detection module, Particle Swarm Optimization (PSO) algorithm is applied for estimating the discrete Round Trip Paths (RTPs). Along the established RTPs, round trip delay (RTD) time values are estimated. Then based on the RTD, the suspected nodes are identified. In fault confirmation module, the nodes are confirmed to be either in FAULTY or ACTIVE state. In fault recovery module, the primary controller (PC) will establish an alternate route via the secondary controllers (SC) by excluding the faulty nodes. Then, it will resend the stored packets to the sink via the newly established route. By experimental results, it is shown that the HDFR framework achieves better detection accuracy and packet delivery ratio.

Autonomous self-healing polyisoprene elastomers with high modulus and good toughness based on the synergy of dynamic ionic crosslinks and highly disordered crystals

2020 ◽  
Vol 11 (41) ◽  
pp. 6549-6558
Author(s):  
Yohei Miwa ◽  
Mayu Yamada ◽  
Yu Shinke ◽  
Shoichi Kutsumizu
Keyword(s):  
Mechanical Properties ◽  
Room Temperature ◽  
Self Healing ◽  
High Modulus ◽  
Disordered Crystals ◽  
Ionic Crosslinks

We designed a novel polyisoprene elastomer with high mechanical properties and autonomous self-healing capability at room temperature facilitated by the coexistence of dynamic ionic crosslinks and crystalline components that slowly reassembled.

Congenital self-healing histiocytosis

1982 ◽  
Vol 118 (4) ◽  
pp. 267-272 ◽  
Author(s):  
E. Bonifazi
Keyword(s):  
Self Healing
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