CN109238728B - Method and system for diagnosing faults of parts on vehicle engine - Google Patents
Method and system for diagnosing faults of parts on vehicle engine Download PDFInfo
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- CN109238728B CN109238728B CN201811064106.2A CN201811064106A CN109238728B CN 109238728 B CN109238728 B CN 109238728B CN 201811064106 A CN201811064106 A CN 201811064106A CN 109238728 B CN109238728 B CN 109238728B
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Abstract
The invention provides a method for diagnosing faults of parts on a vehicle engine, which comprises the steps that a wireless acceleration sensor collects time domain vibration signals generated by parts to be tested on the vehicle engine and forwards the signals in a wireless mode; the fault diagnosis instrument receives a time domain vibration signal sent by the wireless acceleration sensor, and carries out three steps of processing on the signal: firstly, carrying out first period fuzzy C-means clustering filtering on a signal to eliminate background noise and strong natural periodic pulses; secondly, performing second period fuzzy C-means clustering filtering on the signals after the first period filtering to select a frequency band with strongest periodic impact, namely a frequency band with the most concentrated fault characteristics; and thirdly, demodulating the signal subjected to the menstruation and bi-cycle filtering by using Hilbert envelope, and comparing the signal with a preset fault frequency to identify the fault type. By implementing the invention, the problem of complicated wiring existing in the traditional fault diagnosis technology can be solved, and the fault can be quickly and effectively diagnosed by improving the fault feature extraction algorithm.
Description
Technical Field
The invention relates to the technical field of automobile fault detection, in particular to a method and a system for diagnosing faults of parts on an automobile engine.
Background
Like a human body, the vehicle engine is also 'sick' in the long-term running process. The healthy operation of the vehicle engine can not only ensure the safety of vehicles and personnel and generate huge social benefits, but also reduce the environmental pollution and the downtime and generate huge economic benefits. Therefore, it is significant to diagnose a failure of the vehicle engine.
Mechanical fault diagnosis technology is as important for normal operation of machines as medical diagnosis technology for human health, and multiple kinds of state information of mechanical equipment are generally required to be collected to carry out accurate fault diagnosis on the mechanical equipment. Since the state information of the mechanical equipment is reflected by the following common signal types, such as vibration signals, pressure signals, acoustic emission signals, current signals, temperature field signals and the like, the vibration signals are most commonly used in the common signal types and can also reflect effective state information, the mechanical fault diagnosis technology based on the vibration signals has great research value and development potential.
The periodic pulse in the vibration signal has important significance for fault diagnosis of various mechanical equipment. However, in operation, 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, the fault feature extraction is a difficulty which is practically applied to the fault diagnosis of the vehicle engine.
The traditional fault diagnosis technology is not only limited to a plurality of monitoring points, but also leads to the problem of complex wiring caused by the fact that diagnosis objects are not together, especially in some monitoring points which are not easy to contact or special environments (such as radioactive high-risk environments) which cannot be directly wired and monitored, the traditional fault diagnosis technology is difficult and serious, and is also limited to a fault feature extraction algorithm, so that errors are easy to occur in fault diagnosis.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method and a system for diagnosing a fault of a component on an automotive engine, which can not only solve the problem of complex wiring existing in the conventional fault diagnosis technology, but also realize quick and effective diagnosis of the fault by improving a fault feature extraction algorithm.
In order to solve the technical problem, an embodiment of the present invention provides a method for diagnosing a fault of a component on a vehicle engine, where the method includes the following steps:
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.
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 stop bands, and performing primary filtering on the time domain vibration signals to obtain the time domain vibration signals after the primary filtering specifically comprises the following steps:
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.
Selecting the 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, and performing secondary filtering on the time domain vibration signal subjected to primary filtering to obtain a time domain vibration signal subjected to secondary filtering, wherein 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.
The specific steps of 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 tested according to the comparison result are as follows:
if the fault characteristic frequency is matched with the preset fault frequency, determining that the fault of the current fault condition of the part to be tested exists; otherwise, the fault characteristic frequency is not matched with the preset fault frequency, and the fact that the fault of the current fault condition of the part to be tested does not exist is determined.
Wherein the method further comprises:
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.
The embodiment of the invention also provides a system for diagnosing the fault of the part on the vehicle engine, which comprises a wireless acceleration sensor and a fault diagnostic instrument; wherein the content of the first and second substances,
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.
Selecting a frequency band with the minimum skewness value 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.
And 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 frequency band with the most concentrated fault characteristics.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the wireless acceleration sensor and the fault diagnosis instrument are utilized for instant communication, vibration signals are transmitted and processed in real time, the operation is simple and convenient, wiring is not needed, the state monitoring and the real-time fault diagnosis of parts to be detected on the vehicle engine are realized, the skewness is used as a filtering index, the interference of environmental noise and natural periodic pulses is filtered by double-period fuzzy C-means clustering filtering, the fault characteristic frequency is demodulated by using Hilbert envelope, the fault is rapidly and accurately diagnosed by comparing the preset fault frequency, and the problem of low fault diagnosis precision caused by the interference of the environmental noise and the natural periodic vibration of the vehicle engine in actual operation is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
FIG. 1 is a flow chart of a method for diagnosing faults of components on a vehicle engine according to an embodiment of the present invention;
FIG. 2 is a comparison graph of the effect of the method for diagnosing the fault of the component on the vehicle engine before and after the fault signal processing of the tensioner roller of the vehicle five-cylinder engine provided by the embodiment of the invention; wherein, 2a is an effect graph before treatment; 2b is an effect graph after treatment;
fig. 3 is a schematic structural diagram of a system for diagnosing a fault of a component on a vehicle engine according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, in an embodiment of the present invention, a method for diagnosing a fault of a component on a vehicle engine is provided, where the method includes the following steps:
step S1, the wireless acceleration sensor collects time domain vibration signals generated by parts to be detected on the vehicle engine, and forwards the time domain vibration signals to the fault diagnosis instrument in a wireless mode;
the specific process is that a plurality of wireless acceleration sensors can be fixed near the part to be detected of the vehicle engine, so that a time domain vibration signal generated by the part to be detected can be obtained, and the time domain vibration signal is forwarded to the fault diagnosis instrument in a wireless mode.
The acceleration sensor is packaged by a high-sensitivity Micro-Electro-mechanical system (MEMS) chip, an intelligent low-power-consumption mode and a Field-Programmable gate array (FPGA) control circuit are adopted to perform high-speed data processing in real time, 2.4Ghz transmission is supported, a large-capacity storage unit is arranged in the acceleration sensor, and the real-time transmission of data is guaranteed not to lose.
Step S2, the fault diagnosis instrument receives a time domain vibration signal sent by the wireless acceleration sensor, analyzes the 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 the skewness values corresponding to the first frequency band, the second frequency band and the third frequency band which are respectively inversely changed into time domain signals, and realizes primary filtering on the time domain vibration signal to obtain a primarily filtered time domain vibration signal;
the method comprises the specific process that a built-in wireless data receiving module of the fault diagnosis instrument is directly communicated with a wireless acceleration sensor, and an embedded signal processing program carries out first-period fuzzy C-means clustering filtering on acquired real-time domain vibration signals by taking skewness as an index. For removing background noise and strong natural periodic pulses.
Decomposing a 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 by using a preset fuzzy C-means clustering algorithm (such as an FCM function in Matlab);
converting 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 respectively calculating the skewness values of the converted first frequency band, second frequency band and third frequency band;
the skewness is defined as:
the formula (1) shows that: the skewness index is similar to a commonly used kurtosis index, and can identify impact components in the signal, wherein the background noise has a smaller skewness value for the flat distribution, and the impact components have a larger skewness value for the peak distribution.
The method comprises the steps of selecting a frequency band with the minimum deviation value as a frequency band of background noise (as a stop band of a lower limit frequency) from a first frequency band, a second frequency band and a third frequency band, and selecting a frequency band with the maximum deviation value as a frequency band of a natural periodic pulse (as a stop band of an upper limit frequency) from the first frequency band, the second frequency band and the third frequency band, so that signals which are larger than the frequency band with the minimum deviation value in the first frequency band, the second frequency band and the third frequency band and smaller than the frequency band with the maximum deviation value in the first frequency band, the second frequency band and the third frequency band in a time domain vibration signal pass through the frequency band, and the time domain vibration signal after primary filtering is obtained, and therefore two frequency bands containing the maximum environmental noise and the natural periodic pulse are filtered.
Step S3, 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 as a pass band, so that secondary filtering of the time domain vibration signal after primary filtering is realized, and a time domain vibration signal after secondary filtering is obtained;
the specific process is that the second period fuzzy C-means clustering filtering is used for selecting the frequency band with the strongest periodic impact, namely the frequency band with the most concentrated fault characteristics, from the signals after the first period filtering.
Decomposing a frequency spectrum obtained by performing frequency domain analysis on the time domain vibration signal into a fourth frequency band, a fifth frequency band and a sixth frequency band by using a preset fuzzy C-means clustering algorithm (such as an FCM function in Matlab);
transforming the fourth frequency band, the fifth frequency band and the sixth frequency band back to time domain signals by using an inverse Fourier transform algorithm, and respectively calculating the deflection values transformed by the fourth frequency band, the fifth frequency band and the sixth frequency band through a formula (1);
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 frequency band with the most concentrated fault features, so that signals of the frequency band which is greater than the maximum skewness 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 to obtain time domain vibration signals after secondary filtering, and further extracting the frequency band with the most concentrated fault features.
Step S4, the fault diagnosis instrument demodulates the time domain vibration signal after the secondary filtration by adopting a preset Hilbert envelope, the frequency output by demodulation is taken as a fault characteristic frequency and is compared with a preset fault frequency, and further the current fault condition of the part to be detected is determined according to the comparison result; wherein the fault condition is a fault existence or a fault nonexistence.
The method comprises the specific processes that a Hilbert envelope is adopted for demodulating a time domain vibration signal after secondary filtering, the frequency output by demodulation is used as a fault characteristic frequency and is compared with a preset fault frequency, if the fault characteristic frequency is matched with the preset fault frequency, the fault of the current fault condition of a part to be detected is determined to exist, and at the moment, a fault diagnosis instrument reminds maintenance personnel of overhauling through pictures and texts and a whistling alarm; otherwise, the fault characteristic frequency is not matched with the preset fault frequency, and the fact that the fault of the current fault condition of the part to be tested does not exist is determined.
As shown in fig. 2, a comparison graph of the effect before and after the fault signal processing of the bearing roller of the tensioner of the five-cylinder engine for the vehicle is used for the method for diagnosing the fault of the component on the vehicle engine provided by the embodiment of the invention; wherein, 2a is an effect graph before treatment; and 2b is an effect graph after treatment. By contrast, when the Hilbert envelope demodulation is directly carried out without adopting the method for diagnosing the fault of the part on the vehicle engine (as shown in FIG. 2 a), the fault frequency (129Hz) is submerged by the natural periodic pulse frequency (229Hz), and the fault cannot be diagnosed; the method for diagnosing the faults of the parts on the vehicle engine can clearly see the fault frequency.
The information of the roller bearing is as follows: the number of the rolling bodies is 18, the bearing pressure angle is 0 degree, the diameter of the rolling bodies is 5.2 mm, and the diameter of the bearing pitch circle is 30.2 mm.
Bearing roller failure frequency frThe calculation formula is as follows:
the formula (2) can calculate the fault frequency of the bearing roller in the example to be 129.06Hz, and the fault frequency of the bearing roller is 129.06Hz as the preset fault frequency in the fault diagnosis instrument, from the graph in FIG. 2b, the fault frequency can be rapidly distinguished to be 129Hz and 129.06Hz, and the type of the fault of the tension pulley of the engine is judged to be the fault of the bearing roller.
As shown in fig. 3, in an embodiment of the present invention, a system for diagnosing faults of components on an engine of a vehicle is provided, where the system includes a wireless acceleration sensor 210 and a fault diagnostic apparatus 220; wherein the content of the first and second substances,
the wireless acceleration sensor 210 is configured to collect a time-domain vibration signal generated by a component to be tested on the vehicle engine, and forward the time-domain vibration signal to the fault diagnosis instrument 220 in a wireless manner;
the fault diagnosis instrument 220 is configured to receive the time-domain vibration signal sent by the wireless acceleration sensor 210, analyze the time-domain vibration signal to obtain a frequency spectrum when the time-domain vibration signal becomes a frequency-domain signal, decompose 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 select, as a stop band, a frequency band with a minimum skew value and a frequency band with a maximum skew value from the first frequency band, the second frequency band, and the third frequency band after calculating respective skew values corresponding to the first frequency band, the second frequency band, and the third frequency band when the first frequency band is inverted into the time-domain signal, thereby implementing primary filtering on the time-domain vibration signal to obtain a time-domain vibration signal after primary filtering;
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.
Selecting a frequency band with the minimum skewness value 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.
And 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 frequency band with the most concentrated fault characteristics.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the wireless acceleration sensor and the fault diagnosis instrument are utilized for instant communication, vibration signals are transmitted and processed in real time, the operation is simple and convenient, wiring is not needed, the state monitoring and the real-time fault diagnosis of parts to be detected on the vehicle engine are realized, the skewness is used as a filtering index, the interference of environmental noise and natural periodic pulses is filtered by double-period fuzzy C-means clustering filtering, the fault characteristic frequency is demodulated by using Hilbert envelope, the fault is rapidly and accurately diagnosed by comparing the preset fault frequency, and the problem of low fault diagnosis precision caused by the interference of the environmental noise and the natural periodic vibration of the vehicle engine in actual operation is solved.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
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 content of the first and second substances,
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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