Using neural networks in reliability prediction

IEEE Software ◽  
1992 ◽  
Vol 9 (4) ◽  
pp. 53-59 ◽  
Author(s):  
N. Karunanithi ◽  
D. Whitley ◽  
Y.K. Malaiya
Keyword(s):  
Neural Networks ◽  
Reliability Prediction

MODELING AND RELIABILITY PREDICTION FOR THE STEP-STRESS DEGRADATION MEASUREMENTS USING NEURAL NETWORKS METHODOLOGY

1999 ◽  
Vol 06 (03) ◽  
pp. 277-288 ◽  
Author(s):  
DONG SHANG CHANG ◽  
CHIH-CHOU CHIU ◽  
SHWU-TZY JIANG
Keyword(s):  
Neural Networks ◽  
Light Emitting Diode ◽  
Degradation Process ◽  
Reliability Prediction ◽  
Testing Time ◽  
Life Testing ◽  
Light Emitting ◽  
Nonparametric Approach ◽  
Stress Degradation ◽  
Degradation Testing

Rapidly evolving accelerated degradation testing (ADT) has provided an efficient channel for reliability assessment without the labor of conventional life testing that observations are complete or incomplete data. In step-stress loading, a specimen is subjected to successively higher level stress until an appropriate termination time. All specimens go through the same specified pattern of stress levels and testing time. A step-stress degradation testing (SSDT) is corresponding to collect the degradation measurements over time from a step-stress life testing before any specimen fail. The advantages of SSDT may shorten the testing time and reduce the test units compared to ADT. Conventional statistical approach for modeling the step-stress degradation process may be complicated. In this paper, we address a nonparametric approach by applying the methodology of neural networks to calibrate the step-stress degradation measurements and conducting the reliability prediction at use condition. A light emitting diode (LED) example is given to illustrate the implementation procedure.

AN EFFECTIVE EARLY SOFTWARE RELIABILITY PREDICTION PROCEDURE FOR PROCESS ORIENTED DEVELOPMENT AT PROTOTYPE LEVEL EMPLOYING ARTIFICIAL NEURAL NETWORKS

2011 ◽  
Vol 18 (03) ◽  
pp. 237-250 ◽  
Author(s):  
K. KRISHNA MOHAN ◽  
A. K. VERMA ◽  
A. SRIVIDYA
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Reliability ◽  
Heterogeneous Systems ◽  
Reliability Prediction ◽  
Feed Forward Neural Network ◽  
Distributed Components ◽  
Software Reliability Prediction ◽  
Artificial Neural ◽  
Process Oriented

Reliability of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. In this context, reliability prediction of software process oriented systems assumes prime importance. It is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems while building distributed based applications. This paper presents an algorithm using feed-forward neural network for early qualitative software reliability prediction. The inputs for neural networks consist of techno-complexity, practitioner's level, creation effort, size and leakage defects. The number of defects detected in each cycle can be predicted by using Artificial Neural Networks (ANN). Illustrative examples prove the effectiveness of the methodology employed.

Software Reliability Prediction using Neural Networks

2012 ◽  
Vol 60 (7) ◽  
pp. 44-48
Author(s):  
V. Ramakrishna ◽  
M R Narasinga Rao ◽  
T M Padmaja
Keyword(s):  
Neural Networks ◽  
Software Reliability ◽  
Reliability Prediction ◽  
Software Reliability Prediction

Helicopter Reliability Researching Based on Neural Networks

2014 ◽  
Vol 651-653 ◽  
pp. 747-750
Author(s):  
Hong Bin Zhang ◽  
Yan Qiu Ju ◽  
Xue Feng Cao ◽  
Chi Qi
Keyword(s):  
Neural Networks ◽  
Prediction Accuracy ◽  
Data Distribution ◽  
Fuzzy Neural Networks ◽  
Reliability Prediction ◽  
Distribution Model ◽  
Reliability Data ◽  
Fuzzy Neural ◽  
The Neural Networks ◽  
Helicopter Reliability

Neural networks application in helicopter reliability researching is introduced in this paper. Because of high cost and long collected time, the collecting of helicopter reliability data is difficult, so it is difficult to establish the reliability data distribution model, predict the reliability and allocate the reliability of helicopter. By using the neural networks, the reliability data of helicopter is extended. The reliability data distribution model can be established, the reliability prediction can also be done accurately. It is proved by the instance that the reliability prediction accuracy is improved 98.4% by using the fuzzy neural networks. The application of neural networks has a good reference to helicopter reliability researching.

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