Using immunology principles for fault detection

Branco, P. J. Costa; Dente, J. A.; Mendes, Raphael

doi:10.1109/tie.2003.809418

Cited by 56 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An artificial immune system may learn and memorize data pattern of input (antigen). Our approach is that initial antibodies (detectors) are produced at random with negative selection principle [3] . Mutation and maturation of the antibodies happens via further stimulation of antigens.…”

Section: Evolution Learning Based On Artificial Immune Theorymentioning

confidence: 99%

“…For the sake of multiformity of antibodies, initial detectors are generated at random with negative selection process [3] . If a detector vector matchs one of the first kind of antigens, then the vector is deleted meanwhile A new detector is generated at random subsequently as far as enough quantity of initial detectors are brought.…”

Section: A Parameter and Character Of Object Detectedmentioning

confidence: 99%

“…The model and algorithm are applied to clustering and recognition of data patterns triumphantly. P.J.Costa Branco, J.A.Dente and R.Vilela Mends developed a two modules (T and B) algorithm based on immunological principles [3] . This algorithm is used in detection of anomalous situation for diagnostic purpose.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Model For Detection and Diagnosis of Fault Based on Artificial Immune Theory

Chen

Deling

2006

2006 International Conference on Mechatronics and Automation

View full text Add to dashboard Cite

An immune learning algorithm using feature vector code is developed to solve problems about anomaly detection. The antigens input are classified as self pattern code (the first kind of antigens ) and non-self pattern code(the second kind of antigens ). The first kind of antigens is used to generate randomly initial antibodies according to negative selection principle .The second kind of antigens is regarded as learning stylebook of the immune system . Regarding Taking the set of each era antibodies mutated in the system learning as a random series, the condition of convergence of the series and a proof are presented. The astringency of the algorithm is proved. The experimental result indicates that the algorithm can realize optimization to distribution situation of the antibodies and clustering of data modes. High veracity of anomaly detection is obtained.

show abstract

Section: Evolution Learning Based On Artificial Immune Theorymentioning

confidence: 99%

Section: A Parameter and Character Of Object Detectedmentioning

confidence: 99%

See 1 more Smart Citation

A Model For Detection and Diagnosis of Fault Based on Artificial Immune Theory

Chen

Deling

2006

2006 International Conference on Mechatronics and Automation

View full text Add to dashboard Cite

show abstract

“…As the intelligence algorithms such as hidden Markov models (HMM) [6], artificial neural networks (ANN) [7,8], fuzzy clustering methods [9][10][11], support vector machines (SVM) [12], and immune networks [13][14][15][16] have been widely used in many fields.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Based on Particle Swarm Optimization by Artificial Immunisation Algorithm

Chu-jiao

Xia

Xuan

2009

2009 International Conference on Multimedia Information Networking and Security

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is a new general machine-learning tool under the evolutionary algorithms (EAs) and gained lots of attention in various engineering applications, this paper presents an intelligent methodology for diagnosing incipient faults in mine hoist. Artificial immunisation algorithm (AIA) is used to optimise the parameters in PSO in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the PSO classifier is improved. The fault diagnosis of mine hosit shows that the PSO optimised by AIA can give higher recognition accuracy than the normal PSO.

show abstract

“…The former is obtained from the use of differential that represent the physical behavior of system components, while the latter is constructed by using numerical techniques of system identification [1][2][3][4].As the intelligence algorithms such as hidden Markov models (HMM) [5], artificial neural networks (ANN) [6,7], fuzzy clustering methods [8][9][10], support vector machines (SVM) [11], and immune networks [12][13][14][15] have been widely used in many fields. Input-output models employing the intelligence algorithms offer a powerful tool to cope with the modeling problem as well as to make fault diagnostic.…”

Section: Introductionmentioning

confidence: 99%

Immune Model-Based Fault Diagnosis

Chu-jiao

Xia

2008

2008 International Conference on MultiMedia and Information Technology

View full text Add to dashboard Cite

This paper presents an intelligent methodology for diagnosing incipient faults. In this fault diagnosis system, in order to enhance the immune algorithms performance, we propose the improved immune-based symbiotic a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a particle swarm optimization (PSO) technique to improve the mutation mechanism. The application of real-valued negative selection algorithms to simulated and real-world systems is considered. These algorithms deal with the self-nonself discrimination problem in immunity computing, where normal process behaviour is coded as the self and any deviations from normal behaviour is encoded as nonself. The performance of the proposed method is demonstrated using simulation data and compared with other methods. The classification results showed that the proposed method outperforms traditional PSO-based method.

show abstract

Using immunology principles for fault detection

Cited by 56 publications

References 23 publications

A Model For Detection and Diagnosis of Fault Based on Artificial Immune Theory

A Model For Detection and Diagnosis of Fault Based on Artificial Immune Theory

Fault Diagnosis Based on Particle Swarm Optimization by Artificial Immunisation Algorithm

Immune Model-Based Fault Diagnosis

Contact Info

Product

Resources

About