CN109238728B - Method and system for diagnosing faults of parts on vehicle engine - Google Patents

Abstract

The invention provides a method for fault diagnosis of components on a vehicle engine, comprising: a wireless acceleration sensor collects time domain vibration signals generated by a component to be tested on the vehicle engine, and transmits them wirelessly; a fault diagnosis instrument receives the transmission from the wireless acceleration sensor The time-domain vibration signal is obtained, and the signal is processed in three steps: first, the first-period fuzzy C-means cluster filtering is performed on the signal to eliminate background noise and strong natural periodic pulses; The signal is subjected to the second-period fuzzy C-means cluster filtering to select the frequency band with the strongest periodic impact, that is, the frequency band with the most concentrated fault features; 3. The signal after double-period filtering is demodulated by the Hilbert envelope , and compare with the preset fault frequency to identify the fault type. The implementation of the present invention can not only solve the cumbersome wiring problem existing in the traditional fault diagnosis technology, but also realize fast and effective fault diagnosis by improving the fault feature extraction algorithm.

Description

Method and system for fault diagnosis of components on a vehicle engine

technical field

The invention relates to the technical field of automobile fault detection, in particular to a method and system for fault diagnosis of components on a vehicle engine.

Background technique

Just like the human body, a car engine will "sick" during long-term operation. The healthy operation of vehicle engines can not only ensure the safety of vehicles and personnel, and generate huge social benefits, but also reduce environmental pollution, reduce downtime, and generate huge economic benefits. Therefore, it is of great significance to carry out fault diagnosis of vehicle engine.

The mechanical fault diagnosis technology is as important to the normal operation of the machine as the medical diagnosis technology is to the human health. It is usually necessary to collect various status information of the mechanical equipment to carry out accurate fault diagnosis. In view of the above state information of mechanical equipment is reflected by the following common signal types, such as vibration signal, pressure signal, acoustic emission signal, current signal, temperature field signal, etc., and vibration signal is the most commonly used among the above common signal types. Therefore, the mechanical fault diagnosis technology based on vibration signal has great research value and development potential.

Periodic pulses in vibration signals are of great significance for fault diagnosis of various mechanical equipment. However, under operating conditions, the vibration signal of a vehicle engine is often contaminated by background noise and natural periodic pulses caused by periodically moving parts (pistons, connecting rods, cams, etc.). Therefore, fault feature extraction is a difficult point for practical application in vehicle engine fault diagnosis.

The traditional fault diagnosis technology is not limited to many monitoring points, and the diagnosis objects are often not together, which leads to the problem of very cumbersome wiring, especially in some monitoring points that are not easily accessible or special environments that cannot be directly monitored by wiring (such as radioactivity). and other high-risk environments), it is even more difficult, and it is limited to the algorithm of fault feature extraction, which makes fault diagnosis prone to errors.

SUMMARY OF THE INVENTION

The technical problem to be solved by the embodiments of the present invention is to provide a method and system for fault diagnosis of components on a vehicle engine, which can not only solve the cumbersome wiring problem existing in the traditional fault diagnosis technology, but also improve the fault feature extraction algorithm by improving the fault feature extraction algorithm. Realize fast and effective fault diagnosis.

In order to solve the above-mentioned technical problems, the embodiment of the present invention provides a method for diagnosing faults of components on a vehicle engine, and the method includes the following steps:

The wireless acceleration sensor collects the time-domain vibration signal generated by the component to be tested on the vehicle engine, and wirelessly forwards the time-domain vibration signal to the fault diagnosis instrument;

The fault diagnosis instrument receives the time-domain vibration signal sent by the wireless acceleration sensor, analyzes and obtains the frequency spectrum when the time-domain vibration signal becomes a frequency-domain signal, and uses a preset fuzzy C-means clustering algorithm. The frequency spectrum obtained by the frequency domain analysis of the time domain vibration signal is decomposed into a first frequency band, a second frequency band and a third frequency band, and when it is calculated that the first frequency band, the second frequency band and the third frequency band are respectively inverted into time domain signals After each corresponding skewness value, the frequency band with the smallest skewness value and the frequency band with the maximum skewness value are selected from the first frequency band, the second frequency band and the third frequency band as the stop band, so as to realize the The time-domain vibration signal is first filtered to obtain the time-domain vibration signal after the initial filtering;

The fault diagnosis instrument analyzes and obtains the frequency spectrum of the first filtered time domain vibration signal corresponding to the frequency domain signal, and uses the preset fuzzy C-means clustering algorithm to obtain the first filtered time domain. The frequency spectrum obtained by the frequency domain analysis of the vibration signal is decomposed into a fourth frequency band, a fifth frequency band and a sixth frequency band, and it is to be calculated that the fourth frequency band, the fifth frequency band and the sixth frequency band are respectively inverted into time domain signals. After the skewness value is determined, the frequency band with the maximum skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band as the passband, so as to realize the secondary filtering of the time domain vibration signal after the initial filtering. Filter to obtain the time-domain vibration signal after secondary filtering;

The fault diagnosis instrument uses a preset Hilbert envelope to demodulate the time-domain vibration signal after the secondary filtering, and uses the demodulated output frequency as the fault characteristic frequency and compares it with the preset fault frequency , and further according to the comparison result, the current fault condition of the component to be tested is determined; wherein, the fault condition is whether the fault exists or the fault does not exist.

Wherein, selecting the frequency band with the smallest skewness value and the frequency band with the maximum skewness value from the first frequency band, the second frequency band and the third frequency band as the stop band, so as to realize the time domain vibration signal. The specific steps for obtaining the time-domain vibration signal after the initial filtering are as follows:

The frequency band with the smallest skewness value is selected from the first frequency band, the second frequency band and the third frequency band as the frequency band of the background noise, and the skewness degree is selected from the first frequency band, the second frequency band and the third frequency band The frequency band with the largest value is the frequency band of the natural periodic pulse, so that the frequency band in the time domain vibration signal is greater than the minimum skewness value in the first frequency band, the second frequency band and the third frequency band and is smaller than the frequency band from the first frequency band, the second frequency band and the third frequency band. The signal of the frequency band with the largest skewness value among the frequency bands, the second frequency band and the third frequency band is passed to obtain the time domain vibration signal after the primary filtering.

Wherein, the frequency band with the largest skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band as the passband, so as to realize the secondary filtering of the time domain vibration signal after the initial filtering, and obtain The specific steps of the time-domain vibration signal after secondary filtering are:

The frequency band with the largest skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band as the frequency band with the most concentrated fault features, so that the time domain vibration signal after the initial filtering is larger than the fourth frequency band , the fifth frequency band and the sixth frequency band, the signal of the frequency band with the largest skewness value is passed through, and the time domain vibration signal after secondary filtering is obtained.

Wherein, the frequency of the demodulation output is used as the fault characteristic frequency and compared with the preset fault frequency, and further according to the comparison result, the specific steps of determining the current fault condition of the component to be tested are as follows:

If the fault characteristic frequency matches the preset fault frequency, it is determined that the current fault condition of the component to be tested exists; otherwise, the fault characteristic frequency does not match the preset fault frequency. If it matches, it is determined that the current fault condition of the component to be tested does not exist.

Wherein, the method further includes:

When it is determined that the current fault condition of the component to be tested exists, the fault diagnosis instrument reminds maintenance personnel to overhaul through graphic text and whistle alarms.

An embodiment of the present invention also provides a system for diagnosing component faults on a vehicle engine, the system comprising a wireless acceleration sensor and a fault diagnosis instrument; wherein,

The wireless acceleration sensor is used to collect the time-domain vibration signal generated by the component to be tested on the vehicle engine, and wirelessly forward the time-domain vibration signal to the fault diagnosis instrument;

The fault diagnosis instrument is used to receive the time-domain vibration signal sent by the wireless acceleration sensor, analyze and obtain the frequency spectrum when the time-domain vibration signal becomes a frequency-domain signal, and use a preset fuzzy C-means clustering algorithm Decompose the frequency spectrum obtained by the frequency domain analysis of the time domain vibration signal into a first frequency band, a second frequency band and a third frequency band, and to calculate the time when the first frequency band, the second frequency band and the third frequency band are respectively inverted to After obtaining the corresponding skewness values in the domain signal, the frequency band with the minimum skewness value and the frequency band with the maximum skewness value are selected from the first frequency band, the second frequency band and the third frequency band as the stop band to realize Perform initial filtering on the time-domain vibration signal to obtain a time-domain vibration signal after the initial filtering;

The analysis obtains the frequency spectrum of the time-domain vibration signal after the initial filtering corresponding to the frequency domain signal, and the time-domain vibration signal after the initial filtering is processed in the frequency domain through the preset fuzzy C-means clustering algorithm. The spectrum obtained by the analysis is decomposed into a fourth frequency band, a fifth frequency band, and a sixth frequency band, and the corresponding skewness values when the fourth frequency band, the fifth frequency band, and the sixth frequency band are respectively inverted into time domain signals are to be calculated. Then, from the fourth frequency band, the fifth frequency band and the sixth frequency band, the frequency band with the largest skewness value is selected as the passband, and the time domain vibration signal after the initial filtering is filtered twice to obtain a secondary the filtered time-domain vibration signal; and

The time domain vibration signal after the secondary filtering is demodulated by using the preset Hilbert envelope, and the frequency of the demodulation output is used as the fault characteristic frequency and compared with the preset fault frequency, and further according to the comparison As a result, the current fault condition of the component to be tested is determined; wherein, the fault condition is the presence or absence of a fault.

Wherein, the frequency band with the smallest skewness value is selected from the first frequency band, the second frequency band and the third frequency band as the frequency band of the background noise; the skewness value is selected from the first frequency band, the second frequency band and the third frequency band The frequency band with the largest degree value is the frequency band of the natural periodic pulse.

Wherein, the frequency band with the largest skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band as the frequency band with the most concentrated fault features.

Implementing the embodiment of the present invention has the following beneficial effects:

In the embodiment of the present invention, not only the instant communication between the wireless acceleration sensor and the fault diagnosis instrument is used, the vibration signal is transmitted and processed in real time, the operation is simple and no wiring is required, and the condition monitoring and real-time fault diagnosis of the parts to be tested on the vehicle engine are realized. , the skewness is also used as the filter index, and the interference of environmental noise and natural periodic pulses is filtered out by dual-period fuzzy C-means clustering filtering, and then the Hilbert envelope is used to demodulate the fault characteristic frequency. The fault frequency realizes fast and accurate fault diagnosis, and solves the problem of low fault diagnosis accuracy caused by the interference of environmental noise and natural periodic vibration in the actual operation of the vehicle engine.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention, and for those of ordinary skill in the art, obtaining other drawings according to these drawings still belongs to the scope of the present invention without any creative effort.

FIG. 1 is a flowchart of a method for diagnosing component faults on a vehicle engine provided by an embodiment of the present invention;

2 is a comparison diagram of effects before and after the method for diagnosing faults of components on a vehicle engine provided by an embodiment of the present invention is applied to a five-cylinder engine tensioner bearing roller fault signal for a vehicle; wherein, 2a is an effect diagram before processing ; 2b is the effect diagram after processing;

FIG. 3 is a schematic structural diagram of a system for diagnosing component faults on a vehicle engine according to an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

As shown in FIG. 1 , in an embodiment of the present invention, a method for diagnosing faults of components on a vehicle engine is provided, and the method includes the following steps:

Step S1, the wireless acceleration sensor collects the time-domain vibration signal generated by the component to be tested on the vehicle engine, and wirelessly forwards the time-domain vibration signal to the fault diagnosis instrument;

The specific process is that several wireless acceleration sensors can be fixed near the parts to be tested of the vehicle engine, so that the time-domain vibration signals generated by the parts to be tested can be obtained, and the time-domain vibration signals can be wirelessly forwarded to Troubleshooter.

This type of acceleration sensor is packaged with a high-sensitivity Micro-Electro-Mechanical System (MEMS) chip, adopts an intelligent low-power mode, and a Field-Programmable Gate Array (FPGA) control circuit to perform high-speed data processing in real time. , support 2.4Ghz transmission, built-in large-capacity storage unit, to ensure real-time data transmission is not lost.

Step S2, the fault diagnosis instrument receives the time-domain vibration signal sent by the wireless acceleration sensor, analyzes and obtains the frequency spectrum when the time-domain vibration signal becomes a frequency-domain signal, and uses a preset fuzzy C-means clustering algorithm. Decompose the frequency spectrum obtained by the frequency domain analysis of the time domain vibration signal into a first frequency band, a second frequency band and a third frequency band, and to calculate the time when the first frequency band, the second frequency band and the third frequency band are respectively inverted to After obtaining the corresponding skewness values in the domain signal, the frequency band with the minimum skewness value and the frequency band with the maximum skewness value are selected from the first frequency band, the second frequency band and the third frequency band as the stop band to realize Perform initial filtering on the time-domain vibration signal to obtain a time-domain vibration signal after the initial filtering;

The specific process is as follows: the built-in wireless data receiving module of the fault diagnosis instrument communicates directly with the wireless acceleration sensor, and the built-in signal processing program uses the skewness as an index to perform the first-period fuzzy C-means clustering on the collected real-time time-domain vibration signals filter. Used to remove background noise and strong natural periodic pulses.

The spectrum obtained by the frequency domain analysis of the time domain vibration signal is decomposed into a first frequency band, a second frequency band and a third frequency band by a preset fuzzy C-means clustering algorithm (such as the FCM function in Matlab);

Transform the first frequency band, the second frequency band and the third frequency band back to time domain signals by using an inverse Fourier transform algorithm, and calculate the skewness values of the first frequency band, the second frequency band and the third frequency band transformed back respectively;

Skewness is defined as:

It can be seen from formula (1) that the skewness index is similar to the commonly used kurtosis index, which can identify the shock component in the signal. The background noise is a flat distribution with a small skewness value, and the shock component is a peak distribution with a relatively high value. Large skewness values.

The frequency band with the smallest skewness value is selected from the first frequency band, the second frequency band and the third frequency band as the frequency band of the background noise (the stop band as the lower limit frequency), and the first frequency band, the second frequency band and the third frequency band are selected from the first frequency band, the second frequency band and the third frequency band. The frequency band with the largest skewness value is selected as the frequency band of the natural periodic pulse (as the stopband of the upper limit frequency), so that the frequency band in the time domain vibration signal is greater than the minimum skewness value in the first frequency band, the second frequency band and the third frequency band. The signal of the frequency band and less than the frequency band with the maximum skewness value from the first frequency band, the second frequency band and the third frequency band is passed to obtain the time domain vibration signal after the initial filtering, so as to filter out the environmental noise and the most natural periodic pulses. two frequency bands.

Step S3, the fault diagnosis instrument analyzes and obtains the frequency spectrum of the time domain vibration signal after the initial filtering corresponding to the frequency domain signal, and uses the preset fuzzy C-means clustering algorithm. The spectrum obtained by frequency domain analysis of the time domain vibration signal is decomposed into the fourth frequency band, the fifth frequency band and the sixth frequency band, and the fourth frequency band, the fifth frequency band and the sixth frequency band to be calculated are respectively inverted into time domain signals After the corresponding skewness values are obtained, select the frequency band with the maximum skewness value from the fourth frequency band, the fifth frequency band and the sixth frequency band as the passband, so as to realize the time domain vibration signal after the initial filtering. Perform secondary filtering to obtain the time-domain vibration signal after secondary filtering;

The specific process is that the second-period fuzzy C-means cluster filtering is used to select the frequency band with the strongest periodic impact from the signals filtered by the first period, that is, the frequency band with the most concentrated fault features.

The spectrum obtained by the frequency domain analysis of the time domain vibration signal is decomposed into the fourth frequency band, the fifth frequency band and the sixth frequency band by the preset fuzzy C-means clustering algorithm (such as the FCM function in Matlab);

Using the inverse Fourier transform algorithm to transform the fourth, fifth and sixth frequency bands back to time domain signals, and by formula (1), calculate the skew of the fourth, fifth and sixth frequency bands transformed back, respectively degree value;

The frequency band with the largest skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band as the frequency band with the most concentrated fault features, so that the time domain vibration signal after the initial filtering is larger than the fourth frequency band and the fifth frequency band. Pass through the signal of the frequency band with the largest skewness value in the sixth frequency band to obtain the time-domain vibration signal after secondary filtering, so as to extract the frequency band with the most concentrated fault features.

Step S4, the fault diagnosis instrument uses the preset Hilbert envelope to demodulate the time-domain vibration signal after the secondary filtering, and uses the frequency of the demodulation output as the fault characteristic frequency and the preset fault frequency. The frequency is compared, and further according to the comparison result, the current fault condition of the component to be tested is determined; wherein, the fault condition is the presence or absence of the fault.

The specific process is to use the Hilbert envelope to demodulate the time-domain vibration signal after secondary filtering, and use the frequency output from the demodulation as the fault characteristic frequency and compare it with the preset fault frequency. If the preset fault frequency matches, it is determined that the current fault condition of the component to be tested exists. At this time, the fault diagnosis instrument also reminds the maintenance personnel to repair through graphic text and whistle alarm; otherwise, the fault characteristic frequency is the same as the preset fault frequency. If the fault frequency does not match, it is determined that the current fault condition of the component to be tested does not exist.

As shown in FIG. 2 , the method for diagnosing the faults of components on a vehicle engine provided by the embodiment of the present invention is used to compare the effects of the five-cylinder engine tensioner bearing roller fault signal processing before and after processing; wherein, 2a is the processing The renderings before; 2b is the renderings after processing. By comparison, it can be found that the Hilbert envelope demodulation (as shown in Fig. 2a) is directly performed without using the method for diagnosing the faults of components on the vehicle engine of the embodiment of the present invention, and the fault frequency (129 Hz) is naturally periodic pulsed. The frequency (229Hz) is submerged, and the fault cannot be diagnosed; however, the fault frequency can be clearly seen by using the method for diagnosing the fault of the components on the vehicle engine according to the embodiment of the present invention.

The information of the roller bearing is as follows: the number of rolling elements is 18, the bearing pressure angle is 0 degrees, the diameter of the rolling elements is 5.2 mm, and the diameter of the bearing pitch circle is 30.2 mm.

The calculation formula of bearing roller fault frequency _fr is:

In the formula, f is the rotation frequency, α is the bearing pressure angle, d is the diameter of the rolling element, and E is the diameter of the bearing pitch circle. According to formula (2), the fault frequency of the bearing roller in this example can be calculated as 129.06Hz, and the fault frequency 129.06Hz is used as the preset fault frequency in the fault diagnosis instrument. From Figure 2b, it can be quickly identified that the frequency of the fault is similar to 129 Hz and 129.06 Hz, and it is determined that the fault type of the engine tensioner is the bearing roller fault.

As shown in FIG. 3 , in an embodiment of the present invention, a system for diagnosing faults of components on a vehicle engine is provided. The system includes a wireless acceleration sensor 210 and a fault diagnosis instrument 220 ; wherein,

The wireless acceleration sensor 210 is used to collect the time-domain vibration signal generated by the component to be tested on the vehicle engine, and wirelessly forward the time-domain vibration signal to the fault diagnosis instrument 220;

The fault diagnosis instrument 220 is used to receive the time-domain vibration signal sent by the wireless acceleration sensor 210, analyze and obtain the frequency spectrum when the time-domain vibration signal becomes a frequency-domain signal, and gather the signals through a preset fuzzy C-mean value. A class algorithm decomposes the frequency spectrum obtained by the frequency domain analysis of the time domain vibration signal into a first frequency band, a second frequency band and a third frequency band, and the first frequency band, the second frequency band and the third frequency band are calculated to be inverted respectively. After the corresponding skewness values of the time domain signal, select the frequency band with the smallest skewness value and the frequency band with the maximum skewness value from the first frequency band, the second frequency band and the third frequency band as the stop band , realize the initial filtering of the time-domain vibration signal, and obtain the time-domain vibration signal after the initial filtering;

The analysis obtains the frequency spectrum of the time-domain vibration signal after the initial filtering corresponding to the frequency domain signal, and the time-domain vibration signal after the initial filtering is processed in the frequency domain through the preset fuzzy C-means clustering algorithm. The spectrum obtained by the analysis is decomposed into a fourth frequency band, a fifth frequency band, and a sixth frequency band, and the corresponding skewness values when the fourth frequency band, the fifth frequency band, and the sixth frequency band are respectively inverted into time domain signals are to be calculated. Then, from the fourth frequency band, the fifth frequency band and the sixth frequency band, the frequency band with the largest skewness value is selected as the passband, and the time domain vibration signal after the initial filtering is filtered twice to obtain a secondary the filtered time-domain vibration signal; and

The time domain vibration signal after the secondary filtering is demodulated by using the preset Hilbert envelope, and the frequency of the demodulation output is used as the fault characteristic frequency and compared with the preset fault frequency, and further according to the comparison As a result, the current fault condition of the component to be tested is determined; wherein, the fault condition is the presence or absence of a fault.

Wherein, the frequency band with the smallest skewness value is selected from the first frequency band, the second frequency band and the third frequency band as the frequency band of the background noise; the skewness value is selected from the first frequency band, the second frequency band and the third frequency band The frequency band with the largest degree value is the frequency band of the natural periodic pulse.

Wherein, the frequency band with the largest skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band as the frequency band with the most concentrated fault features.

Implementing the embodiment of the present invention has the following beneficial effects:

In the embodiment of the present invention, not only the instant communication between the wireless acceleration sensor and the fault diagnosis instrument is used, the vibration signal is transmitted and processed in real time, the operation is simple and no wiring is required, and the condition monitoring and real-time fault diagnosis of the parts to be tested on the vehicle engine are realized. , the skewness is also used as the filter index, and the interference of environmental noise and natural periodic pulses is filtered out by dual-period fuzzy C-means clustering filtering, and then the Hilbert envelope is used to demodulate the fault characteristic frequency. The fault frequency realizes fast and accurate fault diagnosis, and solves the problem of low fault diagnosis accuracy caused by the interference of environmental noise and natural periodic vibration in the actual operation of the vehicle engine.

Those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be implemented by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage Media such as ROM/RAM, magnetic disk, optical disk, etc.

The above disclosures are only preferred embodiments of the present invention, and of course, the scope of the rights of the present invention cannot be limited by this. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims (7)

1. A method for diagnosing a failure of a component on an engine for a vehicle, comprising the steps of:

the method comprises the following steps that a wireless acceleration sensor collects time domain vibration signals generated by parts to be detected on an automobile engine, and forwards the time domain vibration signals to a fault diagnosis instrument in a wireless mode;

the fault diagnosis instrument receives a time domain vibration signal sent by the wireless acceleration sensor, analyzes the time domain vibration signal to obtain a frequency spectrum when the time domain vibration signal becomes a frequency domain signal, decomposes the frequency spectrum obtained by performing frequency domain analysis on the time domain vibration signal into a first frequency band, a second frequency band and a third frequency band through a preset fuzzy C-means clustering algorithm, selects a frequency band with the minimum skewness value and a frequency band with the maximum skewness value from the first frequency band, the second frequency band and the third frequency band as stop bands after calculating corresponding skewness values when the first frequency band, the second frequency band and the third frequency band are respectively inversely changed into time domain signals, and realizes primary filtering on the time domain vibration signal to obtain a time domain vibration signal after primary filtering;

the fault diagnosis instrument analyzes to obtain a frequency spectrum when the time domain vibration signal after primary filtering is correspondingly changed into a frequency domain signal, the frequency spectrum obtained by performing frequency domain analysis on the time domain vibration signal after primary filtering is decomposed into a fourth frequency band, a fifth frequency band and a sixth frequency band through the preset fuzzy C-means clustering algorithm, and after calculating the skewness values respectively corresponding to the fourth frequency band, the fifth frequency band and the sixth frequency band when the fourth frequency band, the fifth frequency band and the sixth frequency band are respectively inversely changed into the time domain signal, a frequency band with the maximum skewness value is selected from the fourth frequency band, the fifth frequency band and the sixth frequency band to serve as a pass band, so that secondary filtering on the time domain vibration signal after primary filtering is realized, and a time domain vibration signal after secondary filtering is obtained;

the fault diagnosis instrument demodulates the time domain vibration signal after the secondary filtering by adopting a preset Hilbert envelope, takes the frequency output by demodulation as fault characteristic frequency and compares the fault characteristic frequency with a preset fault frequency, and further determines the current fault condition of the part to be detected according to the comparison result; wherein the fault condition is a fault existence or a fault nonexistence.

2. The method for diagnosing the faults of the components on the vehicle engine as recited in claim 1, wherein the step of selecting the frequency band with the minimum skewness value and the frequency band with the maximum skewness value from the first frequency band, the second frequency band and the third frequency band as the stop band to perform the primary filtering on the time-domain vibration signal to obtain the primarily filtered time-domain vibration signal comprises the specific steps of:

and selecting a frequency band with the minimum deflection value as a frequency band of background noise from the first frequency band, the second frequency band and the third frequency band, and selecting a frequency band with the maximum deflection value from the first frequency band, the second frequency band and the third frequency band as a frequency band of natural periodic pulses, so that signals which are greater than the frequency band with the minimum deflection value in the first frequency band, the second frequency band and the third frequency band and less than the frequency band with the maximum deflection value in the first frequency band, the second frequency band and the third frequency band in the time domain vibration signal pass through the frequency band, and the time domain vibration signal after primary filtering is obtained.

3. The method for diagnosing the fault of the component on the vehicle engine as recited in claim 1, wherein the step of selecting the frequency band with the maximum skewness value from the fourth frequency band, the fifth frequency band and the sixth frequency band as the pass band to perform the secondary filtering on the primarily filtered time-domain vibration signal to obtain the secondarily filtered time-domain vibration signal comprises the specific steps of:

and selecting a frequency band with the maximum deflection value from the fourth frequency band, the fifth frequency band and the sixth frequency band as a frequency band with the most concentrated fault characteristics, so that signals of the frequency band which is greater than the maximum deflection value in the fourth frequency band, the fifth frequency band and the sixth frequency band in the time domain vibration signals after primary filtering pass through, and obtaining the time domain vibration signals after secondary filtering.

4. The method for diagnosing a malfunction of a component on an engine for a vehicle according to claim 1, further comprising:

and when the current fault condition of the part to be tested is determined to exist, the fault diagnosis instrument reminds maintenance personnel to overhaul through pictures, texts and whistling alarms.

5. A system for diagnosing faults of parts on a vehicle engine is characterized by comprising a wireless acceleration sensor and a fault diagnostic instrument; wherein,

the wireless acceleration sensor is used for collecting a time domain vibration signal generated by a part to be detected on the vehicle engine and forwarding the time domain vibration signal to the fault diagnosis instrument in a wireless mode;

the fault diagnosis instrument is used for receiving a time domain vibration signal sent by the wireless acceleration sensor, analyzing the time domain vibration signal to obtain a frequency spectrum when the time domain vibration signal becomes a frequency domain signal, decomposing the frequency spectrum obtained by performing frequency domain analysis on the time domain vibration signal into a first frequency band, a second frequency band and a third frequency band through a preset fuzzy C-means clustering algorithm, and selecting a frequency band with the minimum skewness value and a frequency band with the maximum skewness value from the first frequency band, the second frequency band and the third frequency band as stop bands after calculating the corresponding skewness values when the first frequency band, the second frequency band and the third frequency band are respectively inversely changed into time domain signals, so as to realize primary filtering on the time domain vibration signal and obtain a primarily filtered time domain vibration signal;

analyzing to obtain a frequency spectrum when the primarily filtered time domain vibration signal is correspondingly changed into a frequency domain signal, decomposing the frequency spectrum obtained by performing frequency domain analysis on the primarily filtered time domain vibration signal into a fourth frequency band, a fifth frequency band and a sixth frequency band through the preset fuzzy C-means clustering algorithm, and selecting a frequency band with the maximum skewness value from the fourth frequency band, the fifth frequency band and the sixth frequency band as a pass band after calculating the skewness values respectively corresponding to the fourth frequency band, the fifth frequency band and the sixth frequency band when the fourth frequency band, the fifth frequency band and the sixth frequency band are respectively inversely changed into time domain signals, so as to realize secondary filtering on the primarily filtered time domain vibration signal and obtain a secondarily filtered time domain vibration signal; and

demodulating the time domain vibration signal after the secondary filtering by adopting a preset Hilbert envelope, comparing the frequency output by demodulation with a preset fault frequency as a fault characteristic frequency, and further determining the current fault condition of the part to be detected according to the comparison result; wherein the fault condition is a fault existence or a fault nonexistence.

6. The system for diagnosing faults of components on an engine of a vehicle as set forth in claim 5, wherein a frequency band having a minimum skewness value is selected from the first frequency band, the second frequency band, and the third frequency band as a frequency band of background noise; and selecting the frequency band with the maximum deflection value from the first frequency band, the second frequency band and the third frequency band as the frequency band of the natural periodic pulse.

7. The system for diagnosing a malfunction of a component on an engine of a vehicle according to claim 5, wherein a frequency band having a maximum skewness value is selected from the fourth frequency band, the fifth frequency band, and the sixth frequency band as a frequency band in which a malfunction characteristic is most concentrated.

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