Methodology for the Construction of a Rule-Based Knowledge Base Enabling the Selection of Appropriate Bronze Heat Treatment Parameters Using Rough Sets

Górny, Z.; Kluska-Nawarecka, S.; Wilk-Kołodziejczyk, D.; Regulski, K.

doi:10.1515/amm-2015-0050

Cited by 17 publications

(14 citation statements)

References 9 publications

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“…For this reason, the step of classification could be selected in various ways. Most of the obtained feature vectors were separable linearly, therefore many classification methods could be used to solve problems such as: fuzzy logic [46], clustering method [47], nearest mean, k-nearest neighbour classifier [36,48,49], neural network [50][51][52][53][54][55], naive Bayes classifier [56], classifier based on word coding [36], linear discriminant analysis (LDA) [57,58], support vector machine [59,60], rules based on the theory of rough sets [61], Gaussian mixture models (GMM) [62,63]. The authors decided to analyse LDA, nearest neighbour (NN) classifier, and the nearest mean (NM) classi- Fig.…”

Section: Analysed Classifiersmentioning

confidence: 99%

Recognition of rotor damages in a DC motor using acoustic signals

Głowacz

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Diagnosis of electrical direct current motors is essential for industrial plants. The emphasis is put on the development of diagnostic methods of solutions for capturing, processing and recognition of diagnostic signals. This paper presents a technique of early fault diagnosis of a DC motor. The proposed approach is based on acoustic signals. A real-world data of the DC motor were used in the analysis. The work provides an original feature extraction method called the shortened method of frequencies selection (SMoFS-15). The obtained results of the presented analysis show that the early fault diagnostic method can be used for monitoring electrical DC motors. The proposed method can also support other fault diagnosis methods based on thermal, current, and vibration signals.

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Section: Analysed Classifiersmentioning

confidence: 99%

Recognition of rotor damages in a DC motor using acoustic signals

Głowacz

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…The problem of classification was already discussed in literature [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Neural networks were described in many scientific articles [37][38][39][40][41][42].…”

Section: Nearest Neighbour Classifiermentioning

confidence: 99%

Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Głowacz

2016

Archives of Electrical Engineering

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An early fault diagnostic method of Direct Current motors was presented in this article. The proposed method used acoustic signals of a motor. A method of feature extraction called MSAF-RATIO30-EXPANDED (method of selection of amplitudes of frequencies -ratio 30% of maximum of amplitude -expanded) was presented and implemented. An analysis of proposed method was carried out for early fault states of a real DC motor. Four following states of the DC motor were measured and analyzed: the healthy DC motor, DC motor with 3 shorted rotor coils, DC motor with 6 shorted rotor coils, DC motor with a broken coil. Measured states were caused by natural degradation of the DC motor. The obtained results of analysis were good. The presented early fault diagnostic method can be used for protection of DC motors.

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“…Accordingly, the object may certainly not belong to the rough set (if it does not belong to any of the approximations), it may certainly belong to a rough set (if it belongs to both approximations), or the situation may occur when, based on the indicated features, we can not rule out the object membership in a rough set (upper approximation). The approach using rough set theory has shown that, applied to the classification of various methods of the heat treatment of bronze alloy [16,17], this technique gives satisfactory results. It should be noted that building a model of inference for new test materials in a situation where we have only a small number of measurements, as well as incomplete knowledge about the phenomena is difficult and highly biased.…”

Section: Rough Set Theorymentioning

confidence: 99%

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

Rojek

Regulski

Wilk-Kołodziejczyk

et al. 2016

Archives of Foundry Engineering

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One way to ensure the required technical characteristics of castings is the strict control of production parameters affecting the quality of the finished products. If the production process is improperly configured, the resulting defects in castings lead to huge losses. Therefore, from the point of view of economics, it is advisable to use the methods of computational intelligence in the field of quality assurance and adjustment of parameters of future production. At the same time, the development of knowledge in the field of metallurgy, aimed to raise the technical level and efficiency of the manufacture of foundry products, should be followed by the development of information systems to support production processes in order to improve their effectiveness and compliance with the increasingly more stringent requirements of ergonomics, occupational safety, environmental protection and quality. This article is a presentation of artificial intelligence methods used in practical applications related to quality assurance. The problem of control of the production process involves the use of tools such as the induction of decision trees, fuzzy logic, rough set theory, artificial neural networks or case-based reasoning.

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Methodology for the Construction of a Rule-Based Knowledge Base Enabling the Selection of Appropriate Bronze Heat Treatment Parameters Using Rough Sets

Cited by 17 publications

References 9 publications

Recognition of rotor damages in a DC motor using acoustic signals

Recognition of rotor damages in a DC motor using acoustic signals

Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

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