Genetic case-based reasoning for improved mobile phone faults diagnosis

Mohammed, Mazin Abed; Ghani, Mohd Khanapi Abd; Arunkumar, N.; Obaid, Omar Ibrahim; Mostafa, Salama A.; Jaber, Mustafa Musa; Burhanuddin, M. A.; Matar, Bilal Mohammed; abdullatif, Saif khalid; Ibrahim, Dheyaa Ahmed

doi:10.1016/j.compeleceng.2018.07.053

Cited by 63 publications

(23 citation statements)

References 9 publications

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“…Brown [11] proposed an intelligent fault detection system for injection products based on case-based reasoning. Mohammed et al [12] combined genetic algorithms with case-based reasoning to provide experience and knowledge of existing fault diagnosis cases to provide solutions for unknown cases. Qiu et al [13] proposed a modelling method based on the CBR technique to determine the process parameter correction coefficient for small steel grade rolling, which improved the control precision and product quality of the model.…”

Section: Case-based Reasoning Technology and Its Applicationmentioning

confidence: 99%

“…e size, original roughness, original burr, original hardness before machining, roughness after machining, and residual stress all are the numerical data. e similarity is calculated by using formulas (11) and (12). Among the parameters, the size index of the original burr before processing can be characterized by the height or thickness B1 and B2 of the burr [30], and the burr can be quantitatively characterized by measuring the size index of the burr by an instrument.…”

Section: Similarity Calculation Of Case Featuresmentioning

confidence: 99%

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Research on the Optimization Model of the Abrasive Blocks Using Weighted Case‐Based Reasoning

Gao

Yang

Tian

et al. 2019

Mathematical Problems in Engineering

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Barrel finishing process is a universal method to improve the surface quality of parts. It is widely used in high-performance parts of high-end equipment. As a necessary tool consumable for barrel finishing process, the characteristic parameters of the abrasive blocks affect the processing quality and production efficiency. However, the current method for selecting the abrasive blocks requires large number of experiments based on the operator’s extensive experience, which does not meet the rapid development needs of the barrel finishing process. Therefore, this paper proposes a case-based reasoning model with variable weights to achieve the intelligent optimization of the abrasive blocks. Based on the in-depth analysis of the characteristics of the barrel finishing process, a reasonable case base is established firstly, which is to determine the comprehensive case features and the solution of the case. AHP (analytic hierarchy process) is proposed to determine the weight of case features and to dynamically adjust the weight of case features according to the characteristics of the parts to be processed and users’ processing requirements. The results show that the proposed case-based reasoning model with variable weights can quickly, accurately, and reasonably select the abrasive blocks during the process of making processing technique of the barrel finishing, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.

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Section: Case-based Reasoning Technology and Its Applicationmentioning

confidence: 99%

Section: Similarity Calculation Of Case Featuresmentioning

confidence: 99%

Research on the Optimization Model of the Abrasive Blocks Using Weighted Case‐Based Reasoning

Gao

Yang

Tian

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…This unique characteristic makes CBR a promising approach for building intelligent systems [2]. Due to its effectiveness and powerful reasoning capability, CBR has been applied to various fields such as healthcare [3,4,5], fault diagnosis [6,7,8], emergency response [9,10,11], and agricultural management [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Zhai

Ortega

Castillejo

et al. 2019

Sensors

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Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.

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“…The fault inference method can be divided into three types: the artificial intelligence (AI), the fault tree, and a contrast detection. Firstly, AI method generally includes artificial neural networks [10], genetic algorithms (GA) and program design [11,12], the expert system [13,14], the fuzzy rough set theory [15], and other methods. In [10], a neural network was applied to the fault reasoning of a rotating machinery, which employed multisensors' information to implement deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [11] proposed an improved GA to develop power transforms' faults inference, which found the best parameters of the classical domains and weights in the extended clustering method through EGA's search characteristics. Mohammed, M. A. et al [12] proposed 2 Mathematical Problems in Engineering a hybrid scheme that combines the GA with case-based reasoning to improve CBR diagnosis effect. An instinctive transformer's fuzzy expert system for multifault diagnosis was presented [13].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Inference of Inverters Based on a Three‐Layer Bayesian Network

Han

Shuqing

Wang

2019

Mathematical Problems in Engineering

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A three-layer Bayesian intelligent fault inference model (BIFIM) for an inverter is established to infer the probable uncertain faults. The topological structure of the BIFIM includes the inverter’s operation conditions for the first layer, the inverter’s faults for the second layer, and the fault symptoms for the third layer, which combines the field technicians’ knowledge and experiences with historical running data. The prior probability table of the root node is acquired by the method of basic probabilities corrected historical operation data. The conditional probability parameter table of the BIFIM is obtained by the improved maximum expectation algorithm. Four kinds of incomplete evidence were reasoned and verified, including simple evidence with obvious support, incomplete evidence information, complex evidence without obvious support, and evidence with information conflict. The proposed strategy can make use of the available evidences to inference the probabilities of faults, indicating different reasoning abilities under the different degree of completeness of evidence, especially demonstrating the same inference result under some incomplete evidence information as under complete evidence information.

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Genetic case-based reasoning for improved mobile phone faults diagnosis

Cited by 63 publications

References 9 publications

Research on the Optimization Model of the Abrasive Blocks Using Weighted Case‐Based Reasoning

Research on the Optimization Model of the Abrasive Blocks Using Weighted Case‐Based Reasoning

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Intelligent Fault Inference of Inverters Based on a Three‐Layer Bayesian Network

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