Filtering-based intelligent fault detection method for motors in industrial field
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A fault detection and intelligent technology, applied in the direction of motor generator testing, measuring electricity, measuring devices, etc., can solve the problems of large amount of calculation, poor real-time performance, and difficult to describe statistical characteristics.
Active Publication Date: 2020-08-07
JIANGNAN UNIV
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[0003] Traditional fault detection methods assume that noise and disturbance are variables that are known or satisfy a certain probability distribution when performing fault detection. On this basis, fault values are estimated for fault detection. Noise is not a variable that is known or satisfies a certain probability distribution, but it is difficult to describe with specific statistical characteristics, so it will lead to inaccurate detection results
[0004] In order to overcome the problem of inaccurate detection results caused by the fact that the disturbance and noise in the actual operating environment are not variables that are known or satisfy a certain probability distribution, the existing fault detection uses the set membership estimation method for fault detection, using spatial geometry, such as Intervals, ellipsoids, polytopes, etc. describe measurement data, disturbances, and noises, and judge faults by monitoring the consistency between the measured output of the motor and the predicted output of the motor model system, but this method has a large amount of calculation, low accuracy, and real-time gender issues
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Embodiment 1
[0076] This embodiment provides a filter-based intelligent motor fault detection method in the industrial field, see figure 1 , the method includes:
[0077] Step 1: Establish a discrete model of the permanent magnet DC motor.
[0078] According to the working principle of the permanent magnet DC motor, the continuous time nonlinear dynamic model is obtained:
[0079]
[0080] Among them, u represents the armature voltage, K e Represents the counter electromotive force coefficient, n represents the motor speed, R a Represents resistance, i represents current, L 1 Indicates the inductance, K t Indicates the torque coefficient, T 0 and T 2 Represents the no-load torque and load torque respectively, J represents the moment of inertia of the rotor and the load, and Ω represents the angular velocity.
[0081] The no-load torque T produced by the losses of the motor 0 Equivalent to the friction in the bearings or the friction between the brushes and the commutator to prod...
Embodiment 2
[0138] This embodiment provides a filter-based intelligent motor fault detection system in the industrial field, using the intelligent fault detection method described in Embodiment 1 to detect faults on motors, specifically:
[0139] Use the MAX472 current sense amplifier to measure the armature current i when the permanent magnet DC motor is running, and the photoelectric encoder to measure the motor speed n.
[0140] Apply a DC voltage u to the motor, and measure the no-load speed n 0 , no-load current I 0 , no-load time constant T a and load speed n 2 , load current I 2 , the time t required for the armature current to reach 0.95 times the current of the motor in stable operation s , the parameters of the motor are obtained as follows:
[0141] Armature resistance R a for:
[0142]
[0143] Inductance L 1 for:
[0144]
[0145] Coefficient of counter electromotive force K e for:
[0146]
[0147] The viscous friction coefficient f of the motor bearing ...
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Abstract
The invention discloses a filtering-based intelligent fault detection method for motors in the industrial field and belongs to the technical field of fault detection. According to the method, a set membership estimation method is utilized to represent a state feasible set by using vectors, so that prior knowledge of model disturbance and noise does not need to be known in advance, and the practicability and accuracy of the fault detection method are improved; in the solving process of an inversion filtering problem, vectors are used for representing interval boxes; the interval boxes belongingto the feasible set are searched for through the Boolean operation of vector sets, so that the problems of large calculated amount, and the exponential-level increase of calculation time along with increase of interval dimensions of a traditional interval filtering algorithm are solved, and a state interval is estimated more efficiently and accurately. Different from a traditional method for realizing fault detection by utilizing the upper and lower boundaries of estimation residual error, the method provided by the invention obtains the estimated interval of a fault, thereby providing guarantee for subsequent fault diagnosis of the motor by estimating the fault range.
Description
technical field [0001] The invention relates to a filter-based intelligent motor fault detection method in the industrial field, and belongs to the technical field of fault detection. Background technique [0002] The motor realizes the mechanization of production and manufacturing by converting electrical energy into mechanical energy, and is an indispensable part of current industrial production equipment. In order to meet the requirements of increasingly complex and integrated modern automatic control production equipment, real-time and accurate detection of motor faults is required to minimize losses. [0003] Traditional fault detection methods assume that noise and disturbance are variables that are known or satisfy a certain probability distribution when performing fault detection. On this basis, fault values are estimated for fault detection. Noise is not a variable that is known or satisfies a certain probability distribution, but it is difficult to describe with...
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