Applications of computational intelligence to mechanical engineering

Belfiore, Nicola Pio; Rudas, Imre J.

doi:10.1109/cinti.2014.7028702

Cited by 11 publications

(9 citation statements)

References 447 publications

(111 reference statements)

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“…Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most applications [5]. Recent research effort has also been made regarding the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in the rotating machines [6].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Sepulveda¹,

Sinha²

2020

Machines

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Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model.

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Section: Introductionmentioning

confidence: 99%

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Sepulveda¹,

Sinha²

2020

Machines

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“…In other words, CI systems have characteristics that makes it flexible to be utilized in building efficient models in different domains. Some of these characteristics include high computational speed, fault tolerance, adaptation, and ability to error resilience in modeling noisy information [12], [13].…”

Section: Introductionmentioning

confidence: 99%

“…Among of its subsets, Evolutionary Algorithms (EA) are populationbased metaheuristic optimization algorithms which utilize mechanisms such as crossover, selection and mutation. EA are not only used to find solutions for optimization problems but also it can be applied successfully for a various range of other domains such as, in control [12], [15], regression [16], clustering [17] and classification [18].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Nested Genetic-Fuzzy Algorithm Framework for Intrusion Detection and Attacks

2020

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Intrusion Detection System (IDS) plays a very important role in security systems. Among its different types, Network Intrusion Detection System (NIDS) has an effective role in monitoring computer networks systems for malicious and illegal activities. In the literature, the detection of DoS and Probe attacks were with reasonable accuracy in most of the NIDS researches. However, the detection accuracy of other categories of attacks is still low, such as the R2L and U2R in KDDCUP99 dataset along with the Backdoors and Worms in UNSW-NB15 dataset. Computational Intelligence (CI) techniques have the characteristics to address such imprecision problem. In this research, a Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) framework has been developed to produce highly optimized outputs for security experts in classifying both major and minor categories of attacks. The adaptive model is evolved using two-nested Genetic-Fuzzy Algorithms (GFA). Each GFA consists of two-nested Genetic Algorithms (GA). The outer is to evolve fuzzy sets and the inner is to evolve fuzzy rules. The outer GFA assists the inner GFA in training phase, where the best individual in outer GFA interacts with the weak individual in inner GFA to generate new solutions that enhance the prediction of mutated attacks. Both GFA interact together to evolve the best rules for normal, major and minor categories of attacks through the optimization process. Several experiments have been conducted with different settings over different datasets. The obtained results show that the developed model has good accuracy and is more efficient compared with several state-of-the-art techniques.

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“…During the fault pattern recognition stage, many machine learning methods have been proposed in the previous literatures, including k -nearest neighbor [ 30 ], Bayesian decision [ 31 ], artificial neural network (ANN, [ 32 , 33 ]), support vector machine (SVM, [ 34 ]) and so on. Among these recognition techniques, k -nearest neighbor is simple in theory and susceptible to sample distribution.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

Tan

et al. 2018

Entropy

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As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

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Applications of computational intelligence to mechanical engineering

Cited by 11 publications

References 447 publications

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

A Hybrid Nested Genetic-Fuzzy Algorithm Framework for Intrusion Detection and Attacks

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

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