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PCM (possibilistic C-means) clustering algorithm based online automotive suspension performance monitoring method

A car suspension and mean value clustering technology, which is applied in computing, special data processing applications, instruments, etc., can solve problems such as large amount of computing and cannot realize online performance monitoring, and achieve high realizability, low online computing amount, and real strong effect

Inactive Publication Date: 2015-09-30
HARBIN INST OF TECH
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention aims to solve the problem that the existing method has a large amount of calculation and cannot realize online performance monitoring, and provides an online automobile suspension performance monitoring method based on the possibility C-means clustering algorithm

Method used

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  • PCM (possibilistic C-means) clustering algorithm based online automotive suspension performance monitoring method
  • PCM (possibilistic C-means) clustering algorithm based online automotive suspension performance monitoring method
  • PCM (possibilistic C-means) clustering algorithm based online automotive suspension performance monitoring method

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specific Embodiment approach 1

[0013] Specific embodiment one: the on-line automobile suspension performance monitoring method based on possibility C-means clustering algorithm of the present embodiment, it realizes according to the following steps:

[0014] Step 1: Collect N accelerometer samples to obtain normal data when the vehicle suspension is in a healthy state; wherein each accelerometer sample is a four-dimensional vector, one accelerometer sample contains four accelerometer sample values, and each accelerometer sample The value is called an element of the accelerometer sample, each element represents an accelerometer value;

[0015] Step 2: Use the FCM algorithm to calculate the cluster centers of N normal data, use the calculated cluster centers as the initial value of PCM, and calculate the cluster center υ of PCM k and weight η i ; (The first two steps are the offline learning process, followed by the online monitoring process)

[0016] Step 3: During the operation of the car suspension, the ...

specific Embodiment approach 2

[0019] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: FCM calculation is used in the second step

[0020] Calculate the cluster centers of N normal data using the method:

[0021] FCM is not sensitive to initial conditions, X={x i |i=1,2,…,N} is a data set composed of N accelerometer samples, one of which is represented by x i =[x i1 …x in ],x i is a sampling point, there are b data in a sampling point, x in is the bth data;

[0022] FCM completes the clustering of N samples by finding the solution that minimizes the objective function. The objective function is as follows,

[0023] J = Σ k = 1 c Σ i = 1 N ( μ k ...

specific Embodiment approach 3

[0029] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: said step two uses the calculated cluster center as the initial value of PCM, and calculates the cluster center v of PCM k and weight η i Specifically:

[0030] PCM is sensitive to initial conditions. The initial clustering results of FCM are used as the initial value of PCM. When PCM classifies accelerometer sample points, the membership degree of sample points to any clustering category is between 0 and 1. For all The sum of the membership degrees of categories is not 1, therefore, a data sample that does not belong to any known class has a small membership degree for each category;

[0031] PCM achieves clustering by finding the result with the smallest objective function. The objective function is as follows,

[0032] J = Σ i = 1 N ...

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Abstract

The invention relates to a PCM (possibilistic C-means) clustering algorithm based online automotive suspension performance monitoring method and aims to solve the problems of excessive calculation amount and incapacity of online performance monitoring of a conventional method. The method comprises steps as follows: step one, when an automotive suspension is in a healthy state, N accelerometer samples are collected to obtain normal data; step two, a clustering center and a weight of PCM are obtained through calculation; step three, accelerometer values are collected every once in a while during running of the automotive suspension, and the membership degrees mu ki of the accelerometer samples relative to the normal data are calculated according to the clustering center and the weight of the PCM; step four, the number A of the accelerometer samples with the membership degrees lower than a threshold value Thr in a next period of time T is calculated; step five, the collected fault data and normal data are classified with an FDA (force directed algorithm) so as to obtain a classification feature vector wk. The PCM clustering algorithm based online automotive suspension performance monitoring method is applied to the field of automobile performance monitoring.

Description

technical field [0001] The invention relates to an online vehicle suspension performance monitoring method based on the possibility C-means clustering algorithm (PCM). Background technique [0002] With the rapid development of the automobile industry, the comfort, safety and reliability of vehicles have received extensive attention, among which the automobile suspension system is an important part that affects the performance of the automobile. A healthy car suspension system can not only ensure that the car tires tightly grip the ground, but also facilitate braking, and the vibration of the car body during driving is very small, which improves the comfort. If the car suspension system breaks down, the safety and comfort of the car will be greatly reduced. Therefore, the online performance monitoring of the automobile suspension system is very important. At present, the automobile suspension performance monitoring system is still immature, and the model-based monitoring m...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 尹珅黄增辉高会军
Owner HARBIN INST OF TECH
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