Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance

Vališ, David; Žák, Libor; Pokora, Ondřej; Lánský, Petr

doi:10.1016/j.ress.2015.07.026

Cited by 46 publications

(36 citation statements)

References 41 publications

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“…Maintenance, in general, and the condition monitoring, in particular, aims to combine increase of reliability with the lowest costs possible, being direct or indirect. In this type of maintenance, Some relevant topics about related works on condition monitoring and predictive maintenance based on oil analysis, in urban buses fleets, are mentioned and discussed in [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses

et al. 2019

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This paper presents a case study and a model to predict maintenance interventions based on condition monitoring of diesel engine oil in urban buses by accompanying the evolution of its degradation. Many times, under normal functioning conditions, the properties of the lubricants, based on the intervals that manufacturers recommend for its change, are within normal and safety conditions. Then, if the lubricants’ oil condition is adequately accompanied, until reaching the degradation limits, the intervals of oil replacement can be enlarged, meaning that the buses’ availability increases, as well as their corresponding production time. Based on this assumption, a mathematical model to follow and to manage the oil condition is presented, in order to predict the next intervention with the maximum time between them, which means the maximum availability.

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Section: Introductionmentioning

confidence: 99%

Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses

et al. 2019

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“…, where FS-A1, FS-A2, FS-A3, FS-A4-denoted 4 frequency spectra of state A, FS-B1, FS-B2, FS-B3, FS-B4-denoted 4 frequency spectra of state B, FS-C1, FS-C2, FS-C3, FS-C4-denoted 4 frequency spectra of state C, FS-D1, FS-D2, FS-D3, FS-D4-denoted 4 frequency spectra of state D, FS-E1, FS-E2, FS-E3, FS-E4-denoted 4 frequency spectra of state E. Next, 40 differences between frequency spectra are computed: The MSAF-15-MULTIEXPANDED-8-GROUPS found 28 essential frequency components: 48,50,79,81,97,101,128,157,159,1469,1471,1672,1926,1927,1934,1935,1939,1942,1953,1957,1958,1961,1978,2038,2039,2042 Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51]. The results of recognition depended on number of found essential frequency components and selected classification method.…”

Section: Components (Fs-a1 Fs-b1 Fs-c1 Fs-d1 Fs-e1) (Fs-a2 Fs-bmentioning

confidence: 99%

“…x -cmbrc (1) where = [x36, x37, x59, x60, x72, x75, x95, x117, x118, x1092, x1094, x1243, x1432, x1433, x1438, x1439, x1442, x1444, x1452, x1455, x1456, x1458, x1471, x1515, x1516, x1518, x1531, x1894] and training feature vector cmbrc = [cmbrc36, cmbrc37, cmbrc59, cmbrc60, cmbrc72, cmbrc75, cmbrc95, Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51]. The results of recognition depended on number of found essential frequency components and selected classification method.…”

Section: Nearest Neighbour Classifiermentioning

confidence: 99%

Acoustic-Based Fault Diagnosis of Commutator Motor

Głowacz

2018

Electronics

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In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.

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“…Classification methods were discussed in many articles by many researchers [43][44][45][46][47][48][49][50][51][52]. Neural networks were often used for classification problems [53][54][55][56][57][58].…”

Section: Bayes Classifiermentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance

Cited by 46 publications

References 41 publications

Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses

Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses

Acoustic-Based Fault Diagnosis of Commutator Motor

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

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