Large fan running trend analysis and failure prediction method
A trend analysis and fault prediction technology, applied in mechanical equipment, machines/engines, non-variable displacement pumps, etc., can solve problems such as poor real-time performance, low efficiency of online intelligent fault diagnosis, and large data processing capacity, and improve reliability. , to achieve the effect of the prediction of the largest possible failure type
Inactive Publication Date: 2017-09-26
CHONGQING UNIV
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[0005] In view of this, the purpose of the present invention is a large wind turbine operation trend analysis and fault prediction method. The method is aimed at the early faults that are not easy to identify due to the inconspicuous fault representation at the initial stage of the fault, and the online faults caused by the complex analysis process and large amount of data processing. Problems such as low efficiency and poor real-time performance of intelligent fault diagnosis
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[0031] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0032] figure 1 It is a schematic flow chart of the method of the present invention. As shown in the figure, a large-scale wind turbine operation trend analysis and fault prediction method according to the present invention includes the following steps: Step 1: Establish a large-scale wind turbine operating state model; Step 2: Large-scale wind turbine Analysis of wind turbine operation trend; Step 3: Fault prediction of large wind turbines.
[0033] Step 1: Establish a large wind turbine operating state model
[0034] 1) Put T i The vibration-electric parameters collected at all times form the vector k i , then k i can be expressed as k i =[υ 1 ,υ 2 ,…,υ 8 ]. where [υ 1 ,υ 2 ,…,υ 8 ] represents the eigenvector composed of the time-domain features of the vibration parameter and the time-domain feature of the electrical parameter...
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The invention provides a large fan running trend analysis and failure prediction method, and belongs to the field of failure diagnosis. The large fan running trend analysis and failure prediction method aims at solving the problem that an initial failure is difficult to distinguish due to the fact that failure symptoms in the failure initial stage are not obvious. The large fan running trend analysis and failure prediction method comprises the following steps: one, related time domain characteristics of a vibration signal and an electricity parameter are selected and form a state characteristic difference matrix, and states of adjacent time series are described on such base; two, singular values of the difference matrix form a characteristic vector which acts as an input vector of an SVM, and a normal trend and an abnormal trend are subjected to classification analysis; and three, amplitude values under the characteristic frequency are extracted and form a characteristic matrix, HMMs model libraries of different failure modes are established, a maximum likelihood logarithm value is calculated and a maximum likelihood failure causing the abnormal trend is found out, thereby realizing failure prediction. The large fan running trend analysis and failure prediction method plays an important role in guaranteeing fan stable operation, improving maintenance and service efficiency, and guaranteeing personnel and equipment safety.
Description
technical field [0001] The invention belongs to the field of fault diagnosis, in particular to a method for analyzing the running trend of a large fan and predicting a fault. Background technique [0002] A large fan is a large rotary device that converts mechanical energy into pressure energy and kinetic energy of the conveyed gas. It is widely used in mining, metallurgy, chemical industry and other industries and plays an important role. The reliability and continuity of fan operation will directly affect the reliability and safety of industrial production. However, in actual production, due to the influence of factors such as the harsh operating environment of the equipment, equipment aging, and improper installation, fan failures occur from time to time. In addition, the occurrence of serious downtime failures is mostly due to the continuous deterioration of abnormal trends over time. If abnormal operation trends can be analyzed and identified in the early stages of fai...
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IPC IPC(8): F04D27/00G06F17/50
CPCF04D27/001G06F30/20
Inventor 谷振宇朱雪莲胡韶华吕健成金迪文
Owner CHONGQING UNIV