CN112465068A - Rotating equipment fault feature extraction method based on multi-sensor data fusion - Google Patents
Rotating equipment fault feature extraction method based on multi-sensor data fusion Download PDFInfo
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Abstract
The invention discloses a rotating equipment fault feature extraction method based on multi-sensor data fusion, which comprises the following steps: acquiring three-axis vibration displacement information, three-axis vibration acceleration information, noise information and temperature information of the to-be-detected rotating equipment; extracting time domain signals and frequency domain signals of triaxial vibration displacement information, triaxial vibration acceleration information, noise information and temperature information; and taking the frequency domain signal and the time domain signal as fault characteristics for predicting the fault of the rotating equipment to be tested. Compared with the prior art, the method has the advantages that the collected data are subjected to time-frequency domain joint analysis, the fault characteristics are extracted, and the relevance of vibration information with different dimensions is considered. And fault-related factors such as temperature and noise are also considered, so that the prediction result is more accurate. The method can suppress interference under the condition of not reducing frequency domain resolution, and can extract actual characteristic signals under the conditions of deviation of rotating speed and deviation of mechanical parts, so that more tiny hidden troubles of faults can be effectively found.
Description
Technical Field
The invention relates to the technical field of rotating equipment fault prediction, in particular to a rotating equipment fault feature extraction method based on multi-sensor data fusion.
Background
When the rotary mechanical equipment which completes a specific function by rotation is used, tiny faults such as unbalance of rotation of a rotating part, misalignment of the position, abrasion or structural looseness and the like are gradually serious along with the use of the rotary equipment, and the rotary equipment can generate cascading faults possibly; in order to avoid further serious faults and possible cascading faults caused by the serious faults, the working state of the rotating equipment needs to be monitored and diagnosed, and faults are found timely.
At present, diagnosis and analysis of rotating equipment at home and abroad mainly aim at vibration signals, and the analysis method comprises the following steps: waveform analysis, spectral analysis, time-frequency analysis, cepstrum analysis, time series analysis, and the like, which separate time domain information, frequency amplitude information, and frequency domain phase information. The traditional analysis methods not only ignore the relevance of vibration information with different dimensions, but also often ignore other information, such as noise, temperature and the like, besides vibration and related to equipment faults. Therefore, the overall view of fault factors of the rotating equipment cannot be reflected, and even serious distortion and misjudgment can occur.
Therefore, how to ensure that the data for analysis can reflect the overall view of the fault factors of the rotating equipment and improve the accuracy of fault prediction becomes a problem which needs to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the problems actually solved by the present invention include: how to ensure that the data for analysis can reflect the full view of fault factors of the rotating equipment and improve the accuracy of fault prediction.
The invention adopts the following technical scheme:
a rotating equipment fault feature extraction method based on multi-sensor data fusion comprises the following steps:
s1, acquiring three-axis vibration displacement information, three-axis vibration acceleration information, noise information and temperature information of the to-be-detected rotating equipment;
s2, extracting time domain signals of the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information;
s3, extracting frequency domain signals of the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information;
and S4, taking the frequency domain signal and the time domain signal as fault characteristics for predicting the fault of the rotating equipment to be tested.
Preferably, the time domain signal includes a mean, a mean-square, a root-mean-square, a variance, a standard deviation, a probability density function, and a probability distribution function of the three-axis vibration displacement, the three-axis vibration acceleration, the noise and the temperature.
Preferably, the frequency domain signal includes frequency amplitudes and frequency phases of the three-axis vibration displacement, the three-axis vibration acceleration and the noise.
Preferably, filtering the triaxial vibration displacement information, the triaxial vibration acceleration information, the noise information and the temperature information is further included between the step S1 and the step S2.
Preferably, when the rotating equipment to be tested is in different working states, different filtering methods are adopted.
Preferably, the method for filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information includes:
calculating the rotating speed of the rotating equipment to be detected by using an output signal of a magnetic field sensor arranged on the rotating equipment to be detected;
judging the working state of the rotating equipment to be tested based on the rotating speed, wherein the working state comprises stopping working, working at a constant speed and working at a variable speed;
and when the rotating equipment to be tested is in a constant-speed working state, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information by adopting wiener filtering, otherwise, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information by adopting Kalman filtering.
Preferably, the method for extracting fault features of rotating equipment based on multi-sensor data fusion further comprises:
and S5, inputting the fault characteristics into the trained multi-parameter decision tree to predict the fault of the rotating equipment to be tested.
Compared with the prior art, the invention has the following technical advantages:
(1) the invention not only considers the time domain or frequency domain information, but also carries out time-frequency domain combined analysis on the acquired data, extracts the fault characteristics and considers the relevance of the vibration information with different dimensions. Compared with the prior art, the method has the advantages that the vibration information is used as a fault prediction factor, and fault related factors such as temperature and noise are considered, so that the prediction result is more accurate. The method can suppress interference under the condition of not reducing frequency domain resolution, and can extract actual characteristic signals under the conditions of deviation of rotating speed and deviation of mechanical parts, so that more tiny hidden troubles of faults can be effectively found.
(2) The distortion of partial acquired data caused by various external factors during data acquisition is avoided, and therefore, during feature extraction, the acquired data needs to be filtered firstly.
(3) According to the invention, different filtering methods are adopted for the rotating equipment to be tested in different working states, so that the filtering effect can be effectively improved, and the accuracy of final fault prediction is improved.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
FIG. 1 is a flow chart of one embodiment of a method for extracting fault characteristics of rotating equipment based on multi-sensor data fusion, disclosed by the invention;
fig. 2 is a flowchart of another specific embodiment of a rotating equipment fault feature extraction method based on multi-sensor data fusion, which is disclosed by the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a specific embodiment of a rotating device fault feature extraction method based on multi-sensor data fusion disclosed in the present invention includes:
s1, acquiring three-axis vibration displacement information, three-axis vibration acceleration information, noise information and temperature information of the to-be-detected rotating equipment;
s2, extracting time domain signals of the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information;
s3, extracting frequency domain signals of the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information;
and S4, taking the frequency domain signal and the time domain signal as fault characteristics for predicting the fault of the rotating equipment to be tested.
The method for acquiring the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information of the rotating equipment is the prior art and is not described herein again. Compared with the prior art, the method not only considers the time domain or frequency domain information, but also performs time-frequency domain joint analysis on the acquired data, extracts fault characteristics and considers the relevance of vibration information with different dimensions. Compared with the prior art, the method has the advantages that the vibration information is used as a fault prediction factor, and fault related factors such as temperature and noise are considered, so that the prediction result is more accurate. The method can suppress interference under the condition of not reducing frequency domain resolution, and can extract actual characteristic signals under the conditions of deviation of rotating speed and deviation of mechanical parts, so that more tiny hidden troubles of faults can be effectively found.
In specific implementation, the time domain signal includes a mean value, a mean square value, a root mean square value, a variance, a standard deviation, a probability density function, and a probability distribution function of the three-axis vibration displacement, the three-axis vibration acceleration, the noise and the temperature.
The Time domain is a function describing a mathematical function or a physical signal versus Time. For example, a time domain waveform of a signal may express the change of the signal over time. Is the real world and is the only actual domain present. Because our experiences are developed and validated in the time domain, it has become customary for events to occur chronologically. While evaluating the performance of a digital product, the analysis is typically performed in the time domain, since the performance of the product is ultimately measured in the time domain.
In the invention, in order to fully acquire the characteristics for fault prediction, the mean value, the mean square value, the root mean square value, the variance, the standard deviation, the probability density function and the probability distribution function of the three-axis vibration displacement, the three-axis vibration acceleration, the noise and the temperature are used as the fault characteristics of the time domain part.
In specific implementation, the frequency domain signal includes frequency amplitude and frequency phase of three-axis vibration displacement, three-axis vibration acceleration and noise.
The frequency domain is a coordinate system used to describe the frequency characteristics of a signal. In electronics, control system engineering and statistics, frequency domain plots show the amount of signal in each given frequency band over a range of frequencies. Frequency domains, which are used more often in radio frequency and communication systems, are also encountered in high-speed digital applications. The most important properties of the frequency domain are: it is not true, but a mathematical construct. The time domain is the only objective domain, while the frequency domain is a mathematical category that follows certain rules, and is also known by some scholars as the god perspective.
In order to accurately reflect frequency domain signals, the frequency amplitude and the frequency phase of triaxial vibration displacement, triaxial vibration acceleration and noise are selected as the fault characteristics of the frequency domain part.
In order to further optimize the technical solution of the present invention, as shown in fig. 2, in a specific implementation, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information, and the temperature information is further included between step S1 and step S2.
Wave filtering is an operation of filtering specific band frequencies in a signal, and is an important measure for suppressing and preventing interference, and filtering is classified into classical filtering and modern filtering. In the present invention, in order to avoid distortion of partially acquired data due to various external factors during data acquisition, it is first necessary to filter the acquired data during feature extraction.
In specific implementation, different filtering methods are adopted when the rotating equipment to be tested is in different working states.
When the to-be-tested rotating equipment is in different working states, the interference on the acquired data is greatly different, so that different filtering methods are adopted for the to-be-tested rotating equipment in different working states, the filtering effect can be effectively improved, and the accuracy of final fault prediction is improved.
In specific implementation, the method for filtering the triaxial vibration displacement information, the triaxial vibration acceleration information, the noise information and the temperature information comprises the following steps:
calculating the rotating speed of the rotating equipment to be detected by using an output signal of a magnetic field sensor arranged on the rotating equipment to be detected;
judging the working state of the rotating equipment to be tested based on the rotating speed, wherein the working state comprises stopping working, working at a constant speed and working at a variable speed;
and when the rotating equipment to be tested is in a constant-speed working state, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information by adopting wiener filtering, otherwise, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information by adopting Kalman filtering.
The essence of the wiener filtering is to minimize the mean square of the estimation error (defined as the difference between the desired response and the actual output of the filter). Discrete-time wiener filtering theory evolved from the pioneering work of wiener on linear optimal filters of continuous-time signals. The importance of the wiener filter is that it provides a frame of reference for linear filtering of the generalized stationary random signal. The wiener filtering formula is derived through the spectral decomposition of the stationary process, and is difficult to be popularized to the more general non-stationary process and the multidimensional situation, so the application range is limited. On the other hand, when the observation results are increased, it is not easy to obtain a new filter value from the calculated filter value and a new observation value relatively easily, and particularly, the demand for rapidly processing a large amount of data on an electronic computer cannot be satisfied. Therefore, in the invention, the wiener filtering is adopted in the constant-speed working state.
Kalman filtering (Kalman filtering) is an algorithm that uses a linear system state equation to optimally estimate the state of a system by inputting and outputting observation data through the system. The optimal estimation can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system. Data filtering is a data processing technique for removing noise and restoring true data, and Kalman filtering can estimate the state of a dynamic system from a series of data with measurement noise under the condition that measurement variance is known. Because the method is convenient for realizing computer programming and can update and process the data acquired on site in real time, Kalman filtering is the most widely applied filtering method at present and is better applied to the fields of communication, navigation, guidance, control and the like. Therefore, the Kalman filtering is adopted when the device is in a non-uniform working state.
In specific implementation, the method for extracting fault features of rotating equipment based on multi-sensor data fusion further comprises the following steps:
and S5, inputting the fault characteristics into the trained multi-parameter decision tree to predict the fault of the rotating equipment to be tested.
After the fault characteristics are obtained, the fault characteristics are input into the trained multi-parameter decision tree, and then the fault prediction result of the to-be-tested rotating equipment can be obtained. The types of failures mainly include: bearing failure, stator failure, rotor failure, air gap eccentricity.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A rotating equipment fault feature extraction method based on multi-sensor data fusion is characterized by comprising the following steps:
s1, acquiring three-axis vibration displacement information, three-axis vibration acceleration information, noise information and temperature information of the to-be-detected rotating equipment;
s2, extracting time domain signals of the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information;
s3, extracting frequency domain signals of the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information;
and S4, taking the frequency domain signal and the time domain signal as fault characteristics for predicting the fault of the rotating equipment to be tested.
2. The rotating equipment fault feature extraction method based on multi-sensor data fusion of claim 1, wherein the time domain signal comprises a mean, a mean square, a root mean square, a variance, a standard deviation, a probability density function, a probability distribution function of three-axis vibration displacement, three-axis vibration acceleration, noise and temperature.
3. The rotating equipment fault feature extraction method based on multi-sensor data fusion of claim 1, wherein the frequency domain signal comprises frequency amplitudes and frequency phases of three-axis vibration displacement, three-axis vibration acceleration and noise.
4. The rotating equipment fault feature extraction method based on multi-sensor data fusion of claim 1, wherein the step S1 and the step S2 further comprise filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information.
5. The rotating equipment fault feature extraction method based on multi-sensor data fusion as claimed in claim 4, wherein different filtering methods are adopted when the rotating equipment to be tested is in different working states.
6. The rotating equipment fault feature extraction method based on multi-sensor data fusion of claim 5, wherein the method for filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information comprises:
calculating the rotating speed of the rotating equipment to be detected by using an output signal of a magnetic field sensor arranged on the rotating equipment to be detected;
judging the working state of the rotating equipment to be tested based on the rotating speed, wherein the working state comprises stopping working, working at a constant speed and working at a variable speed;
and when the rotating equipment to be tested is in a constant-speed working state, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information by adopting wiener filtering, otherwise, filtering the three-axis vibration displacement information, the three-axis vibration acceleration information, the noise information and the temperature information by adopting Kalman filtering.
7. The multi-sensor data fusion-based rotating equipment fault feature extraction method according to any one of claims 1 to 6, further comprising:
and S5, inputting the fault characteristics into the trained multi-parameter decision tree to predict the fault of the rotating equipment to be tested.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113049252A (en) * | 2021-03-25 | 2021-06-29 | 成都天佑路航轨道交通科技有限公司 | Fault detection method for train bearing box |
| CN114167842A (en) * | 2021-12-08 | 2022-03-11 | 中国船舶科学研究中心 | Fault prediction and health management method based on vibration active control system |
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| JP2007263609A (en) * | 2006-03-27 | 2007-10-11 | Nsk Ltd | Apparatus and method for diagnosing failure of mechanical equipment |
| CN103323274A (en) * | 2013-05-24 | 2013-09-25 | 上海交通大学 | Rotating machinery condition monitoring and fault diagnosing system and method |
| CN104655380A (en) * | 2015-03-16 | 2015-05-27 | 北京六合智汇技术有限责任公司 | Method for extracting fault features of rotating mechanical equipment |
| WO2020141678A1 (en) * | 2018-12-31 | 2020-07-09 | 주식회사 일진글로벌 | Fault diagnosis device and vehicle wheel bearing having same fault diagnosis device |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| JP2007263609A (en) * | 2006-03-27 | 2007-10-11 | Nsk Ltd | Apparatus and method for diagnosing failure of mechanical equipment |
| CN103323274A (en) * | 2013-05-24 | 2013-09-25 | 上海交通大学 | Rotating machinery condition monitoring and fault diagnosing system and method |
| CN104655380A (en) * | 2015-03-16 | 2015-05-27 | 北京六合智汇技术有限责任公司 | Method for extracting fault features of rotating mechanical equipment |
| WO2020141678A1 (en) * | 2018-12-31 | 2020-07-09 | 주식회사 일진글로벌 | Fault diagnosis device and vehicle wheel bearing having same fault diagnosis device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113049252A (en) * | 2021-03-25 | 2021-06-29 | 成都天佑路航轨道交通科技有限公司 | Fault detection method for train bearing box |
| CN114167842A (en) * | 2021-12-08 | 2022-03-11 | 中国船舶科学研究中心 | Fault prediction and health management method based on vibration active control system |
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