CN115310475A - Fault diagnosis method, device, equipment, system and storage medium of crusher - Google Patents
Fault diagnosis method, device, equipment, system and storage medium of crusher Download PDFInfo
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- CN115310475A CN115310475A CN202210691752.1A CN202210691752A CN115310475A CN 115310475 A CN115310475 A CN 115310475A CN 202210691752 A CN202210691752 A CN 202210691752A CN 115310475 A CN115310475 A CN 115310475A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G01M99/008—Subject matter not provided for in other groups of this subclass by doing functionality tests
Abstract
The invention discloses a fault diagnosis method, a fault diagnosis device, equipment, a fault diagnosis system and a storage medium of a crusher. A method of fault diagnosis of a crusher comprising: detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals; determining the fault type of the crusher according to the characteristic signal when at least one of the torque characteristic signal and the rotating speed characteristic signal is detected to be abnormal. By using the method, the fault type of the crusher is determined through the multidimensional signal, and the accuracy of fault identification and diagnosis can be greatly improved.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method, device, equipment, system and storage medium of a crusher.
Background
Automobile generally need carry out crushing treatment to it before the recycle, and the application of large-scale breaker can greatly promote mineral products recovery efficiency and quality, nevertheless because operational environment is abominable, reasons such as the operating strength is high and improper maintenance all can arouse a series of troubles of breaker. If the waste car is not processed in time, the waste car crushing efficiency is low, and the enterprise maintenance cost and the shutdown cost are increased.
At present, in the field of fault diagnosis of crushers, the running state of the crusher is diagnosed in real time by mainly extracting characteristic quantities through audio processing, but the problem of low identification precision or misdiagnosis exists only through identification processing of a single signal.
Disclosure of Invention
The invention provides a fault diagnosis method, a fault diagnosis device, equipment, a fault diagnosis system and a storage medium of a crusher, which are used for determining the fault type of the crusher through a multi-dimensional signal and improving the accuracy of fault diagnosis.
In a first aspect, an embodiment of the present invention provides a fault diagnosis method for a crusher, including:
detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals;
in case an abnormality of at least one of the torque signature and the rotational speed signature is detected, determining a type of malfunction of the crusher depending on the signature.
Optionally, the determining the fault type of the crusher according to the characteristic signal includes:
extracting a first characteristic quantity of the audio characteristic signal and a second characteristic quantity of the vibration characteristic signal;
and if the first characteristic quantity and/or the second characteristic quantity reach the corresponding fault diagnosis threshold value, determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity.
Optionally, the extracting the first feature quantity of the audio feature signal and the second feature quantity of the vibration feature signal includes:
performing time domain analysis and frequency domain analysis on the audio characteristic signals to obtain a plurality of audio characteristic quantities, and selecting a plurality of audio characteristic quantities with highest identification degrees from the plurality of audio characteristic quantities as the first characteristic quantities;
and performing time domain analysis and frequency domain analysis on the vibration characteristic signals to obtain a plurality of vibration characteristic quantities, and selecting a plurality of vibration characteristic quantities with the highest identification degree from the plurality of vibration characteristic quantities as the second characteristic quantities.
Optionally, the fault diagnosis threshold corresponding to the first characteristic quantity and the second characteristic quantity is determined according to a standard vibration signal and a standard audio signal of the crusher in a normal load state, and the detected audio characteristic signal and the detected vibration characteristic signal.
Optionally, the determining, according to the standard vibration signal and the standard audio signal of the crusher in the normal load state and the detected audio characteristic signal and the detected vibration characteristic signal, the fault diagnosis threshold corresponding to the first characteristic quantity and the second characteristic quantity includes:
calculating a first difference degree of the audio characteristic signal and the standard audio signal and a second difference degree of the vibration characteristic signal and the standard vibration signal;
determining a fault diagnosis threshold corresponding to the first characteristic quantity according to the first difference;
and determining a fault diagnosis threshold value corresponding to the second characteristic quantity according to the second difference degree.
Optionally, the determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity includes:
generating a first characteristic matrix of the audio characteristic signal according to the first characteristic quantity;
generating a second characteristic matrix of the vibration characteristic signal according to the second characteristic quantity;
calculating a fault signal matrix according to the first characteristic matrix, the second characteristic matrix, the characteristic matrix of the torque characteristic signal and the characteristic matrix of the rotating speed characteristic signal;
and determining the fault type according to the fault signal matrix.
Optionally, before determining the fault type of the crusher according to the characteristic signal, the method further includes:
collecting an unloaded characteristic signal of the crusher;
and preprocessing the standard vibration signal and the standard audio signal according to the no-load characteristic signal of the crusher, and preprocessing the detected audio characteristic signal and the detected vibration characteristic signal.
Optionally, the material of the crusher is determined from the characteristic signal.
In a second aspect, an embodiment of the present invention further provides a fault diagnosis apparatus for a crusher, including:
the detection module is used for detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals;
the determining module is used for determining the fault type of the crusher according to the characteristic signal when at least one of the torque characteristic signal and the rotating speed characteristic signal is detected to be abnormal.
In a third aspect, an embodiment of the present invention further provides a fault diagnosis device, including:
at least one processor;
and a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of fault diagnosis of a crusher in an embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a fault diagnosis system for a crusher, including:
a crusher;
the vibration sensor is arranged on the crusher and used for acquiring a vibration characteristic signal of the crusher;
the sound pressure sensor is arranged on the crusher and used for acquiring an audio characteristic signal of the crusher;
the rotating speed sensor is arranged on the crusher and used for acquiring a rotating speed characteristic signal of the crusher;
the torque sensor is arranged on the crusher and used for acquiring a torque characteristic signal of the crusher;
and a failure diagnosis apparatus according to an embodiment of the present invention.
In a fifth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the program, when executed by a processor, implements the fault diagnosis method of the crusher according to the embodiment of the present invention.
The embodiment of the invention provides a fault diagnosis method, a fault diagnosis device, equipment, a fault diagnosis system and a storage medium of a crusher, wherein the fault diagnosis method comprises the steps of detecting characteristic signals of the crusher, wherein the characteristic signals comprise an audio characteristic signal, a vibration characteristic signal, a torque characteristic signal and a rotating speed characteristic signal; then, in case at least one of the torque signature and the rotational speed signature is detected as abnormal, a fault type of the crusher is determined from the signature. By using the method, the fault type of the crusher is determined through the multidimensional signal, and the accuracy of fault identification and diagnosis can be greatly improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments 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 obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a fault diagnosis method for a crusher according to an embodiment of the present invention;
fig. 2 is a flowchart of a fault diagnosis method for a crusher according to a second embodiment of the present invention;
FIG. 3 is a complete schematic diagram of a fault diagnosis method for a crusher according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fault diagnosis device of a crusher according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a fault diagnosis device according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of a fault diagnosis system of a crusher according to a fifth embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a fault diagnosis method for a crusher according to an embodiment of the present invention, which is applicable to a fault diagnosis situation of a large crushing device for processing a scraped car, and the method can be performed by a fault diagnosis apparatus for a crusher, which can be implemented in the form of hardware and/or software, and the apparatus can be configured in the fault diagnosis device. As shown in fig. 1, the method includes:
s110, detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals.
Wherein, the crusher can be a roller crusher, a cone crusher and other large crushers which can crush the scraped car. The characteristic signal may be considered as a signal reflecting the operating conditions of the crusher, including an audio characteristic signal, a vibration characteristic signal, a torque characteristic signal and a rotational speed characteristic signal.
Specifically, a sound pressure sensor, a vibration sensor, a rotation speed sensor and a torque sensor mounted on the crusher may be detected by the fault diagnosis apparatus, thereby detecting the characteristic signal of the crusher.
And S120, determining the fault type of the crusher according to the characteristic signal when at least one of the torque characteristic signal and the rotating speed characteristic signal is detected to be abnormal.
It should be noted that when the crusher fails, one or both of the torque characteristic signal and the rotation speed characteristic signal will inevitably show abnormality, because the rotation speed and the torque are the most direct signals for responding to the crushing failure, and when the rotation speed suddenly drops and the torque suddenly increases, the crusher is in a failure state, but the failure cause cannot be accurately given. The no-load signal and the normal load signal of the crushing equipment, and all fault types and corresponding fault characteristic signals of the crushing equipment during crushing operation can be collected in advance and sorted and recorded into the fault diagnosis equipment.
Specifically, because the work object of breaker, for the scraped car shell, tear through the extrusion tentatively, the inner structure is complicated, and the material is inhomogeneous, and uncontrollable factor is too much, consequently, can judge rotational speed and moment of torsion as initial condition, and when rotational speed and moment of torsion are normal, just directly judge that the breaker is in normal condition. And determining the fault type of the crusher according to the characteristic signal when at least one of the torque characteristic signal and the rotating speed characteristic signal is detected to be abnormal.
Optionally, the material of the crusher is determined from the characteristic signal.
Wherein, the material of breaker can be regarded as the sheet metal component on the car, state before advancing the breaker: car shell materials, lump materials, bulk materials and the like.
Specifically, because the characteristic signals of different materials are different when the materials are crushed, the materials of the crusher can be determined according to the characteristic signals. For example, different materials have different hardness, strength, structural uniformity and the like, sound vibration force of crushing equipment is different during crushing, the rotating speed and torque of a crusher are different, the rotating speed with high hardness is slow, and correspondingly, audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals are different. Accordingly, the characteristic signal can be used to identify different materials.
The embodiment of the invention provides a fault diagnosis method of a crusher, which comprises the steps of detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals; then, in case at least one of the torque signature and the rotational speed signature is detected as abnormal, a fault type of the crusher is determined from the signature. By using the method, the fault type of the crusher is determined through the multidimensional signal, and the accuracy of fault identification and diagnosis can be greatly improved.
Example two
Fig. 2 is a flowchart of a fault diagnosis method for a crusher according to a second embodiment of the present invention, which is further optimized based on the above embodiments. It should be noted that, for technical details that are not described in detail in this embodiment, reference may be made to any of the embodiments described above.
As shown in fig. 2, the method includes:
s210, collecting an unloaded characteristic signal of the crusher.
The no-load characteristic signal is a signal when the crusher operates when no crushing object is present, that is, a characteristic signal when only the crusher is kept operating without any load.
S220, preprocessing a standard vibration signal and a standard audio signal according to the no-load characteristic signal of the crusher, and preprocessing the detected audio characteristic signal and the detected vibration characteristic signal.
Wherein the standard vibration signal and the standard audio signal can be considered as characteristic signals of the crusher in a normal load state. The preprocessing of the signal may be considered as performing a conversion unit or a noise reduction process on the signal.
Specifically, since the collected signal is an electrical signal, generally a current or voltage signal, it needs to be converted into a unit, such as converting an audio signal into decibels, converting a vibration acceleration signal into m/s 2 In this case, the original signal is an original signal, and when the analysis processing is performed, noise generated by various fields or devices during operation may be mixed in the signal, so that it is further required to perform noise reduction processing to achieve the purpose of speech enhancement.
And S230, extracting a first characteristic quantity of the audio characteristic signal and a second characteristic quantity of the vibration characteristic signal when at least one of the torque characteristic signal and the rotating speed characteristic signal is detected to be abnormal.
The first feature quantity of the audio feature signal and the second feature quantity of the vibration feature signal may be considered as feature quantities obtained by performing time domain analysis and frequency domain analysis on the audio feature signal and the vibration feature signal. Such as mean, root mean square, peak, kurtosis index, waveform index, pulse factor, etc. By extracting a plurality of characteristic quantities of the characteristic signals, key information of the characteristic signals can be deeply mined, such as the purpose of short-time energy is to distinguish voiced sounds and unvoiced sounds, and the change of different materials generated during crushing can be distinguished during fault diagnosis.
S240, determining fault diagnosis threshold values corresponding to the first characteristic quantity and the second characteristic quantity according to a standard vibration signal and a standard audio signal of the crusher in a normal load state and the detected audio characteristic signal and the detected vibration characteristic signal.
The detected audio characteristic signal and vibration characteristic signal may be regarded as signals in an abnormal load state, i.e., fault signals.
Specifically, the method may compare the normal vibration signal and the standard audio signal in the normal load state, and the detected audio characteristic signal and the vibration characteristic signal, that is, compare the normal signal and the fault signal, and select the characteristic quantity with a large difference as a judgment basis, that is, use the characteristic quantity value (or a certain range of the value, determined according to the actual situation) of the fault signal as a fault diagnosis threshold value to judge whether the crushing device is faulty.
And S250, if the first characteristic quantity and/or the second characteristic quantity reach the corresponding fault diagnosis threshold value, determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity.
The fault diagnosis threshold may be a value or a range of values. The fault type can be judged by fault codes, the fault codes in the normal state are all 0, and the abnormal state is displayed as 1.
Specifically, when the first characteristic quantity and/or the second characteristic quantity reach the corresponding fault diagnosis threshold value, the fault of the crusher is judged, and according to the specific abnormal characteristic value (namely the signal characteristic value with the fault code of 1), the fault type causing the characteristic value to be abnormal is reversely deduced.
Optionally, the extracting a first feature quantity of the audio feature signal and a second feature quantity of the vibration feature signal includes:
carrying out time domain analysis and frequency domain analysis on the audio characteristic signals to obtain a plurality of audio characteristic quantities, and selecting a plurality of audio characteristic quantities with highest identification degrees from the plurality of audio characteristic quantities as first characteristic quantities;
and performing time domain analysis and frequency domain analysis on the vibration characteristic signals to obtain a plurality of vibration characteristic quantities, and selecting a plurality of vibration characteristic quantities with the highest identification degree from the plurality of vibration characteristic quantities to serve as second characteristic quantities.
The characteristic quantities obtained by time domain analysis may be a mean value, a root mean square value, a peak value, a kurtosis index, a waveform index, a pulse factor and the like, and the characteristic quantities obtained by frequency domain analysis may be an amplitude spectrum, a power spectrum, a cepstrum, an envelope spectrum and the like.
Specifically, by comparing the numerical values (maximum value, average value, minimum value) of the feature quantities extracted from the no-load signal, the normal load signal and the fault signal, the feature quantity with a difference of 1 to 2 orders of magnitude can be regarded as a feature quantity with a high degree of recognition, and the feature quantity with the highest degree of recognition is used as the first feature quantity and the second feature quantity.
Optionally, determining the fault diagnosis threshold corresponding to the first characteristic quantity and the second characteristic quantity according to the standard vibration signal and the standard audio signal of the crusher in the normal load state, and the detected audio characteristic signal and the vibration characteristic signal, includes:
calculating a first difference degree of the audio characteristic signal and the standard audio signal and a second difference degree of the vibration characteristic signal and the standard vibration signal;
determining a fault diagnosis threshold corresponding to the first characteristic quantity according to the first difference;
and determining a fault diagnosis threshold corresponding to the second characteristic quantity according to the second difference.
Wherein the first degree of difference and the second degree of difference may be considered as an order of magnitude difference in the values of the characteristic quantities between the signals.
Specifically, the first difference degree and the second difference degree of the signal may be calculated by direct comparison, subtraction, or quotient, and the numerical value or the numerical range of the feature quantity having the highest degree of identification for which the calculated difference is 1 to 2 orders of magnitude or more is used as the corresponding failure diagnosis threshold value.
Optionally, determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity includes:
generating a first characteristic matrix of the audio characteristic signal according to the first characteristic quantity;
generating a second characteristic matrix of the vibration characteristic signal according to the second characteristic quantity;
calculating a fault signal matrix according to the first characteristic matrix, the second characteristic matrix, the characteristic matrix of the torque characteristic signal and the characteristic matrix of the rotating speed characteristic signal;
and determining the fault type according to the fault signal matrix.
A row vector in one characteristic quantity generating matrix generates a first characteristic matrix and a second characteristic matrix according to a plurality of characteristic quantities in audio and vibration signals.
Specifically, different conditions, different materials, different fault types, and conditions fed back by characteristic quantities may differ, so that multiple groups of characteristic quantities need to be selected for determination in analysis. The fault signal matrix can be calculated by applying the following formula, and the fault type can be determined by giving a corresponding fault code according to y.
Wherein, a represents an n × 1 first feature matrix of the audio feature signal, the row vectors in the n × 1 first feature matrix are all feature quantities extracted from the audio signal, the normal value is 0, and the anomaly is 1.b represents an n × 1 second feature matrix of the vibration feature signal. c represents an n × 1 signature matrix of the torque signature. d represents an n × 1 feature matrix of the rotation speed feature signal. The matrix A is used for introducing the audio signal, the rotating speed signal and the torque signal for comprehensive judgment, the matrix B is used for introducing the vibration characteristic signal, the rotating speed signal and the torque signal for comprehensive judgment, each row of the matrix A and the matrix B relates to calculation of a characteristic quantity, and part of pseudo information can be screened out through the introduced rotating speed signal and the introduced torque signal.
The fault codes in the normal state are all 0, the abnormal state can be displayed as 1, some characteristic quantities may be abnormal according to different fault types, and some characteristic quantities still belong to the normal state, so that the abnormal characteristic quantities are deduced according to the fault codes, and the fault type causing the characteristic quantity abnormality is reversely deduced.
Fig. 3 is a complete schematic diagram of a fault diagnosis method for a crusher according to an embodiment of the present invention, as shown in fig. 3, by collecting signals, preprocessing, multidimensional signal, analyzing, processing, setting threshold values, deriving a matrix, and calculating a decision. Firstly, collecting characteristic signals of no-load, load and common faults of equipment, and preprocessing normal load signal data by taking the no-load signal as a pilot signal to obtain a data group I; and (4) preprocessing the fault signal data by taking the no-load signal as a pilot signal to obtain a data group II.
Then, carrying out time domain analysis on the vibration signals in the data I to obtain characteristic parameters (mean value, root mean square value, peak value, kurtosis index, waveform index and pulse factor) frequency domain analysis to obtain characteristic parameters (characteristic parameters: frequency spectrums such as amplitude frequency spectrum, power frequency spectrum, cepstrum, envelope spectrum and the like), and selecting several groups of characteristic quantities with higher identification degree from the characteristic parameters to carry out fault identification; and (3) carrying out time domain and frequency domain analysis on the audio signal in the data I to obtain characteristic parameters (such as short-time energy, short-time zero-crossing rate, short-time autocorrelation, short-time power and the like) with higher identification degree, and identifying the fault.
And then, carrying out time domain and frequency domain analysis on the vibration signal and the audio signal in the data group II, comparing the characteristic quantity, setting a corresponding threshold value, and judging the characteristic quantity to be abnormal when the characteristic quantity exceeds the threshold value.
The multidimensional signal comprehensive processing, the characteristic signals (1), (2) … n of audio and vibration refer to n number of characteristic quantities, wherein the number of the audio characteristic quantity and the number of the vibration characteristic quantity can not be consistent (namely n in the audio is not one with n in the vibration signal, m can be used for replacing n in the vibration signal), and when the matrix calculation is carried out, the middle vacancy is replaced by a 0 matrix. The audio characteristic signal (1) is normally output to be 0, the abnormity is 1, the characteristic signal (2) is normally output to be 0, the abnormity is 1, the characteristic signal (3) is normally output to be 0, the abnormity is 1, the characteristic signal n is normally 0, the abnormity is 1, and a matrix a of n multiplied by 1 is formed. The vibration characteristic signal (1) is normally output as 0, the abnormality is 1, the characteristic signal (2) is normally output as 0, the abnormality is 1, the characteristic signal (3) is normally output as 0, the abnormality is 1, the characteristic signal n is normally 0, the abnormality is 1, and an n × 1 matrix b is formed. The torque signatures form an n × 1 matrix c in the same way. The speed characteristic signals form an n × 1 matrix d.
And calculating a fault signal matrix by using the following formula, and giving a corresponding fault code.
The normal state fault codes are all 0, the abnormal state is displayed as 1, some characteristic quantities may be abnormal according to different fault types, and some characteristic quantities still belong to the normal state, so that the abnormal characteristic quantities are deduced according to the fault codes, and the fault type causing the characteristic quantity abnormality is reversely deduced. In addition, when different materials are crushed, the characteristic quantities of the materials are different, and the purpose of identifying the materials can be realized by taking the characteristic quantities as criteria.
According to the fault diagnosis method for the crusher, when the crusher works actually, material components are complex, other equipment works on site, signal interference is serious, a single signal is used as a fault diagnosis basis, reliability and stability are difficult to guarantee, the characteristic signal of the crusher is detected, and then the fault type of the crusher is determined according to the characteristic signal under the condition that at least one of the torque characteristic signal and the rotating speed characteristic signal is abnormal, namely the accuracy of fault identification and diagnosis can be improved to a great extent through mutual evidence of multi-dimensional signal diagnosis. And the fault types can be increased and decreased according to the actual application condition, the dimensionality of the diagnosis form is changed, and the diagnosis efficiency and accuracy are improved.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a fault diagnosis device of a crusher according to a third embodiment of the present invention. As shown in fig. 4, the apparatus includes: a detection module 41 and a determination module 42.
The detection module 41 is configured to detect a feature signal of the crusher, where the feature signal includes an audio feature signal, a vibration feature signal, a torque feature signal, and a rotation speed feature signal;
a determination module 42 for determining a type of fault of the crusher based on the characteristic signal in case at least one of the torque characteristic signal and the rotational speed characteristic signal is detected as abnormal.
In the embodiment, the device firstly detects the characteristic signals of the crusher through the detection module 41, wherein the characteristic signals comprise an audio characteristic signal, a vibration characteristic signal, a torque characteristic signal and a rotating speed characteristic signal; then, by means of the determination module 42, in case at least one of the torque signature and the rotational speed signature is detected as abnormal, a type of fault of the crusher is determined from the signature. The fault type of the crusher is determined through the multidimensional signals, and the accuracy of fault identification and diagnosis can be greatly improved.
Optionally, the determining module 42 is specifically configured to determine the fault type of the crusher according to the characteristic signal, and includes:
an extraction unit that extracts a first feature quantity of the audio feature signal and a second feature quantity of the vibration feature signal;
and if the first characteristic quantity and/or the second characteristic quantity reach the corresponding fault diagnosis threshold value, determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity.
Optionally, the extracting unit is specifically configured to extract a first feature quantity of the audio feature signal and a second feature quantity of the vibration feature signal, and includes:
carrying out time domain analysis and frequency domain analysis on the audio characteristic signals to obtain a plurality of audio characteristic quantities, and selecting a plurality of audio characteristic quantities with highest identification degrees from the plurality of audio characteristic quantities as first characteristic quantities;
and performing time domain analysis and frequency domain analysis on the vibration characteristic signals to obtain a plurality of vibration characteristic quantities, and selecting a plurality of vibration characteristic quantities with the highest identification degree from the plurality of vibration characteristic quantities to serve as second characteristic quantities.
Optionally, the determining module 42 is further specifically configured to determine the fault diagnosis threshold corresponding to the first characteristic quantity and the second characteristic quantity according to the standard vibration signal and the standard audio signal of the crusher in the normal load state, and the detected audio characteristic signal and the vibration characteristic signal.
Optionally, the determining module 42 is specifically configured to determine the fault diagnosis threshold corresponding to the first characteristic quantity and the second characteristic quantity according to the standard vibration signal and the standard audio signal of the crusher in the normal load state, and the detected audio characteristic signal and the vibration characteristic signal, and includes:
calculating a first difference degree of the audio characteristic signal and the standard audio signal and a second difference degree of the vibration characteristic signal and the standard vibration signal;
determining a fault diagnosis threshold value corresponding to the first characteristic quantity according to the first difference degree;
and determining a fault diagnosis threshold corresponding to the second characteristic quantity according to the second difference.
Optionally, determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity includes:
generating a first characteristic matrix of the audio characteristic signal according to the first characteristic quantity;
generating a second characteristic matrix of the vibration characteristic signal according to the second characteristic quantity;
calculating a fault signal matrix according to the first characteristic matrix, the second characteristic matrix, the characteristic matrix of the torque characteristic signal and the characteristic matrix of the rotating speed characteristic signal;
and determining the fault type according to the fault signal matrix.
Optionally, the determining module 42, before being configured to determine the fault type of the crusher according to the characteristic signal, further includes:
collecting an idle load characteristic signal of the crusher;
and preprocessing the standard vibration signal and the standard audio signal according to the no-load characteristic signal of the crusher, and preprocessing the detected audio characteristic signal and the detected vibration characteristic signal.
Optionally, the determination module 42 may also determine the material of the crusher from the characteristic signal.
The fault diagnosis device of the crusher provided by the embodiment of the invention can execute the fault diagnosis method of the crusher provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 5 shows a schematic structural diagram of the failure diagnosis apparatus 10 that can be used to implement an embodiment of the present invention. The fault diagnosis device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The fault diagnosis device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the fault diagnosing apparatus 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the fault diagnosing apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A plurality of components in the failure diagnosis apparatus 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the fault diagnosing apparatus 10 to exchange information/data with other apparatuses through a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a fault diagnosis method of the crusher.
In some embodiments, the fault diagnosis method of the crusher may be implemented as a computer program, which is tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed on the fault diagnosis device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the crusher fault diagnosis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g. by means of firmware) to perform a fault diagnosis method of the crusher.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described herein may be implemented on a fault diagnosis device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the fault diagnosis apparatus. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
EXAMPLE five
Fig. 6 is a schematic structural diagram of a fault diagnosis system of a crusher according to a fifth embodiment of the present invention. As shown in fig. 6, the fault diagnosis system includes:
a crusher 60;
the vibration sensor 61 is arranged on the crusher 60 and used for acquiring a vibration characteristic signal of the crusher 60;
the sound pressure sensor 62 is arranged on the crusher 60 and is used for acquiring an audio characteristic signal of the crusher 60;
a rotating speed sensor 63 arranged on the crusher 60 and used for acquiring a rotating speed characteristic signal of the crusher 60;
a torque sensor 64 disposed on the crusher 60 for collecting a torque characteristic signal of the crusher 60;
and a failure diagnosis apparatus 10 according to any of the embodiments of the present invention.
In this embodiment, the system first collects corresponding characteristic signals through a plurality of sensors disposed on the crusher, and then detects the characteristic signals of the crusher through the fault diagnosis device 10, where the characteristic signals include an audio characteristic signal, a vibration characteristic signal, a torque characteristic signal, and a rotation speed characteristic signal; and determining the fault type of the crusher according to the characteristic signals under the condition that at least one of the torque characteristic signals and the rotating speed characteristic signals is detected to be abnormal. The fault type of the crusher is determined through the multidimensional signals, and the accuracy of fault identification and diagnosis can be greatly improved.
The fault diagnosis system of the crusher provided by the fifth embodiment can be used for realizing the fault diagnosis method of the crusher provided by any embodiment, and has corresponding functions and beneficial effects.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of fault diagnosis of a crusher, comprising:
detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals;
in case an abnormality of at least one of the torque signature and the rotational speed signature is detected, determining a type of malfunction of the crusher depending on the signature.
2. The method of claim 1, wherein said determining a fault type of the crusher from the signature signal comprises:
extracting a first characteristic quantity of the audio characteristic signal and a second characteristic quantity of the vibration characteristic signal;
and if the first characteristic quantity and/or the second characteristic quantity reach the corresponding fault diagnosis threshold value, determining the fault type of the crusher according to the first characteristic quantity and/or the second characteristic quantity.
3. The method according to claim 2, wherein the extracting the first feature quantity of the audio feature signal and the second feature quantity of the vibration feature signal includes:
performing time domain analysis and frequency domain analysis on the audio characteristic signals to obtain a plurality of audio characteristic quantities, and selecting a plurality of audio characteristic quantities with highest identification degrees from the plurality of audio characteristic quantities as the first characteristic quantities;
and performing time domain analysis and frequency domain analysis on the vibration characteristic signals to obtain a plurality of vibration characteristic quantities, and selecting a plurality of vibration characteristic quantities with the highest identification degree from the plurality of vibration characteristic quantities as the second characteristic quantities.
4. The method of claim 2, further comprising:
and determining fault diagnosis threshold values corresponding to the first characteristic quantity and the second characteristic quantity according to a standard vibration signal and a standard audio signal of the crusher in a normal load state and the detected audio characteristic signal and the vibration characteristic signal.
5. The method according to claim 4, characterized in that the determining fault diagnosis thresholds corresponding to the first and second characteristic quantities from a standard vibration signal and a standard audio signal of the crusher in a normal load state, and the detected audio and vibration characteristic signals comprises:
calculating a first difference degree of the audio characteristic signal and the standard audio signal and a second difference degree of the vibration characteristic signal and the standard vibration signal;
determining a fault diagnosis threshold value corresponding to the first characteristic quantity according to the first difference degree;
and determining a fault diagnosis threshold value corresponding to the second characteristic quantity according to the second difference degree.
6. Method according to claim 2, characterized in that said determining the type of fault of the crusher from said first and/or second characteristic quantity comprises:
generating a first characteristic matrix of the audio characteristic signal according to the first characteristic quantity;
generating a second characteristic matrix of the vibration characteristic signal according to the second characteristic quantity;
calculating a fault signal matrix according to the first characteristic matrix, the second characteristic matrix, the characteristic matrix of the torque characteristic signal and the characteristic matrix of the rotating speed characteristic signal;
and determining the fault type according to the fault signal matrix.
7. The method of claim 4, further comprising, prior to determining the type of fault of the crusher from the signature signal:
collecting a no-load characteristic signal of the crusher;
and preprocessing the standard vibration signal and the standard audio signal according to the no-load characteristic signal of the crusher, and preprocessing the detected audio characteristic signal and the detected vibration characteristic signal.
8. The method of claim 1, further comprising:
determining the material of the crusher according to the characteristic signal.
9. A failure diagnosis device of a crusher, characterized by comprising:
the detection module is used for detecting characteristic signals of the crusher, wherein the characteristic signals comprise audio characteristic signals, vibration characteristic signals, torque characteristic signals and rotating speed characteristic signals;
the determining module is used for determining the fault type of the crusher according to the characteristic signal when at least one of the torque characteristic signal and the rotating speed characteristic signal is detected to be abnormal.
10. A fault diagnosis apparatus characterized by comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of fault diagnosis of a crusher as claimed in any one of claims 1 to 8.
11. A fault diagnosis system of a crusher, characterized by comprising:
a crusher;
the vibration sensor is arranged on the crusher and used for acquiring a vibration characteristic signal of the crusher;
the sound pressure sensor is arranged on the crusher and used for acquiring an audio characteristic signal of the crusher;
the rotating speed sensor is arranged on the crusher and used for acquiring a rotating speed characteristic signal of the crusher;
the torque sensor is arranged on the crusher and used for acquiring a torque characteristic signal of the crusher;
and the failure diagnosis apparatus according to claim 10.
12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method for fault diagnosis of a crusher as claimed in any one of the claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117494027A (en) * | 2023-12-29 | 2024-02-02 | 宁德时代新能源科技股份有限公司 | Method, device, system and storage medium for monitoring fault of stirring device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117494027A (en) * | 2023-12-29 | 2024-02-02 | 宁德时代新能源科技股份有限公司 | Method, device, system and storage medium for monitoring fault of stirring device |
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