Artificial Intelligence in Diagnostics of Electric Machines

Hammer, Milos; Šimková, Marcela; Ministr, M.

doi:10.1007/978-3-642-05022-0_24

Cited by 5 publications

(4 citation statements)

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“…Set or correct the initial data (matrix R of binary relations between the values of the output parameters B (diagnostic parameters) and the values of the input parameters A (technical condition) [ 35 , 36 ]. The diagram of the impact of defects on diagnostic parameters is shown in Figure 1 [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Nikitin

Božek

Peterka

2020

Sensors

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The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.

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Section: Resultsmentioning

confidence: 99%

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Nikitin

Božek

Peterka

2020

Sensors

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“…In [1][2][3][4][5][6][7][8][9][10][11][12] questions of diagnostics for electric motors and processing equipment are considered. Also the attention is paid to find a residual service life by means of fuzzy logic [1,3].…”

Section: Introductionmentioning

confidence: 99%

Development of the Algorithm for Definition of Residual Service Life by a Comprehensive Diagnosis of the Electromechanical Drive

Stepanov¹,

Lagutkin²,

Nikitin³

2015

AMS

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The algorithm for determining the residual service life of the stand, which consists of an asynchronous motor, clutch and worm gear, is developed. As a comprehensive approach the analysis of mechanical and electrical drive diagnostic parameters is considered. The change in vibration speed and spectral coefficients of the wavelet analysis of the stator electric current is investigated under load in the presence and absence of defects. The equation is received to determine the residual service life.

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“…In works [1][2][3][4][5][6][7][8][9][10][11][12][13][14] the questions of diagnostics for bearings and asynchronous motors and also mechanical units are considered. The methods of artificial intelligence [15][16][17][18][19][20] are one of modern instruments for decision-making in the field of technical diagnostics.…”

Section: Introductionmentioning

confidence: 99%