CN108196986B - Equipment abnormality detection method and device, computer equipment and storage medium - Google Patents
Equipment abnormality detection method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method and a device for detecting equipment abnormality, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a time domain monitoring signal obtained by monitoring equipment; carrying out frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal; adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal according to a dimensionless processing algorithm; inputting each level of frequency domain components after amplitude adjustment in the frequency domain monitoring signals into a pre-trained anomaly detection model to obtain an anomaly detection result; and the anomaly detection model learns the corresponding relation between each level of frequency domain component and the anomaly detection result which are adjusted by adopting a dimensionless processing algorithm. The method can realize real-time and automatic abnormality detection on the mass equipment, and improve the efficiency and accuracy of result detection.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a device anomaly detection method and apparatus, a computer device, and a storage medium.
Background
With the continuous development and innovation of computer technology, the technology of internet of things has been greatly developed in many fields. At present, the number of devices accessed to the Internet of things is huge, and how to realize real-time management and monitoring of the states of the devices and detect whether the devices are abnormal has a profound meaning.
In the prior art, the existing abnormal reasons are summarized manually for a sensor with a periodic function output signal in equipment, and then whether the sensor is abnormal or not is judged according to a spectrogram of the output signal of the sensor. In this way, whether the sensor is abnormal or not is judged manually, so that the efficiency and the accuracy are low, the workload is high, and the method is not suitable for detecting mass equipment.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide an apparatus anomaly detection method, so as to implement real-time and automatic anomaly detection on a mass apparatus, and improve the efficiency and accuracy of result detection, so as to solve the technical problems that the existing method for manually judging whether a sensor is anomalous or not has low efficiency and accuracy, and has a large workload, and is not suitable for detecting the mass apparatus.
A second object of the present invention is to provide a device abnormality detection apparatus.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a computer-readable storage medium.
A fifth object of the invention is to propose a computer program product.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides an apparatus anomaly detection method, including:
acquiring a time domain monitoring signal obtained by monitoring equipment;
carrying out frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal;
adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal according to a dimensionless processing algorithm;
inputting each level of frequency domain components with the adjusted amplitudes in the frequency domain monitoring signals into a pre-trained anomaly detection model to obtain anomaly detection results; and the anomaly detection model learns the corresponding relation between each level of frequency domain component adjusted by the dimensionless processing algorithm and the anomaly detection result.
According to the equipment anomaly detection method, the single-dimensional time domain monitoring signals obtained by monitoring the equipment are converted into the multi-dimensional frequency domain monitoring signals, more amplitude-frequency characteristics can be obtained, then the amplitude values of all levels of frequency domain components in the frequency domain monitoring signals are adjusted by using a dimensionless processing algorithm, the amplitude characteristics of the frequency domain components corresponding to the anomaly data can be amplified, finally the frequency domain monitoring signals are detected by using a pre-trained anomaly detection model, the detection result is obtained, and real-time and automatic anomaly detection on mass equipment can be realized. In addition, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result, so that the frequency domain monitoring signals are detected according to the anomaly detection model, the efficiency and the accuracy of result detection can be improved, and the technical problems that in the prior art, whether the sensor is abnormal or not is judged manually, the efficiency and the accuracy are low, the workload is large, and the anomaly detection model is not suitable for detection of mass equipment are solved.
In order to achieve the above object, a device abnormality detection apparatus according to a second embodiment of the present invention includes:
the acquisition module is used for acquiring a time domain monitoring signal obtained by monitoring equipment;
the transformation module is used for carrying out frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal;
the adjusting module is used for adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal according to a dimensionless processing algorithm;
the detection module is used for inputting each level of frequency domain components with the adjusted amplitudes in the frequency domain monitoring signals into a pre-trained anomaly detection model to obtain anomaly detection results; and the anomaly detection model learns the corresponding relation between each level of frequency domain component adjusted by the dimensionless processing algorithm and the anomaly detection result.
The device anomaly detection device provided by the embodiment of the invention can obtain more amplitude-frequency characteristics by converting the single-dimensional time domain monitoring signal obtained by monitoring the device into the multi-dimensional frequency domain monitoring signal, then adjust the amplitude of each level of frequency domain component in the frequency domain monitoring signal by using a dimensionless processing algorithm, amplify the amplitude characteristics of the frequency domain component corresponding to the anomaly data, and finally detect the frequency domain monitoring signal by using a pre-trained anomaly detection model to obtain a detection result, thereby realizing real-time and automatic anomaly detection on mass devices. In addition, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result, so that the frequency domain monitoring signals are detected according to the anomaly detection model, the efficiency and the accuracy of result detection can be improved, and the technical problems that in the prior art, whether the sensor is abnormal or not is judged manually, the efficiency and the accuracy are low, the workload is large, and the anomaly detection model is not suitable for detection of mass equipment are solved.
To achieve the above object, a third embodiment of the present invention provides a computer device, including: the device abnormality detection method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the device abnormality detection method according to an embodiment of the first aspect of the present invention is implemented.
In order to achieve the above object, a fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program is configured to implement the device abnormality detection method according to the first aspect of the present invention when executed by a processor.
To achieve the above object, a fifth embodiment of the present invention provides a computer program product, wherein when instructions of the computer program product are executed by a processor, the method for detecting device anomalies according to the first embodiment of the present invention is performed.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a first method for detecting device anomaly according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a Fourier series expansion of a time-domain monitor signal according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a second method for detecting device anomaly according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus anomaly detection device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of another apparatus abnormality detection device according to an embodiment of the present invention;
FIG. 6 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Aiming at the technical problems that whether a sensor is abnormal or not is judged manually in the prior art, the efficiency and the accuracy are low, the workload is large, and the method is not suitable for detecting mass equipment. In addition, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result, so that the frequency domain monitoring signals are detected according to the anomaly detection model, and the efficiency and accuracy of result detection can be improved.
A device abnormality detection method, apparatus, computer device, and storage medium according to embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a first method for detecting device abnormality according to an embodiment of the present invention.
As shown in fig. 1, the apparatus abnormality detection method includes the steps of:
The equipment abnormity detection method provided by the embodiment of the invention can be used for detecting whether the sensor with the output signal of the periodic function in the equipment is abnormal or not. When the sensor is subjected to abnormality detection, an output signal of the sensor can be acquired, which is recorded as a time-domain monitoring signal in the embodiment of the present invention, and optionally, the time-domain monitoring signal is marked as f (x).
And 102, performing frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal.
In the embodiment of the invention, because the acquired time domain monitoring signal is a periodic function, the time domain monitoring signal can be subjected to Fourier series expansion to obtain a frequency domain monitoring signal, and the frequency domain monitoring signal is a combination of infinite sine waves.
Specifically, fourier series expansion is performed on the time domain monitoring signal f (x), and the obtained frequency domain monitoring signal is:
where k is the frequency domain component
Number of stages of (A)kIs the amplitude.
As an example, referring to fig. 2, fig. 2 is a schematic diagram of a fourier series expansion of a time-domain monitoring signal in an embodiment of the present invention. Waveform 1 represents the time domain monitoring signal, and waveforms 2, 3, 4, etc. represent frequency domain components of each level in the frequency domain monitoring signal obtained by performing fourier series expansion on the time domain monitoring signal.
Further, since f (x) is a periodic function, the natural frequency is more obvious and is in a lower frequency band. Therefore, in the embodiment of the present invention, in order to reduce the workload of the system and improve the processing efficiency, each level of frequency domain components of the frequency domain monitoring signal may be screened, and frequency domain components of a preset number of levels are reserved, where the reserved frequency components of the preset number of levels include frequency domain components of a natural frequency, for example, the preset number of levels is marked as M. Furthermore, because the high-frequency components in the frequency domain components are noise, in the embodiment of the invention, each level of frequency domain components of the frequency domain monitoring signal can be denoised to filter out the frequency domain components with the frequency higher than the preset threshold value, so that the accuracy of the detection result is improved. The preset threshold may be set according to an application scenario of the device.
And 103, adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal according to a dimensionless processing algorithm.
And the dimensionless processing algorithm is used for changing the amplitude of each level of frequency domain component after adjustment into a scalar.
In the embodiment of the invention, after the amplitude of each level of frequency domain component in the frequency domain monitoring signal is adjusted according to the dimensionless processing algorithm, the frequency domain component amplitude characteristic of the natural frequency can be weakened, and the frequency domain component amplitude characteristic corresponding to the abnormal data is amplified. For example, the dimensionless processing algorithm may be a normalization algorithm, or the dimensionless processing algorithm may be any other algorithm that can weaken the frequency domain component amplitude feature of the natural frequency and amplify the frequency domain component amplitude feature corresponding to the abnormal data, which is not limited in this embodiment of the present invention.
In the embodiment of the invention, after the frequency domain monitoring signal is obtained, the amplitude of each level of frequency domain component in the frequency domain monitoring signal can be adjusted according to a dimensionless processing algorithm to obtain the adjusted frequency domain monitoring signal. Optionally, the adjusted frequency domain monitor signal is marked as f' (x).
As a possible implementation manner, a normalization algorithm may be adopted to adjust the amplitudes of the frequency domain components at each level in the frequency domain monitoring signal, so that the amplitude of the frequency domain component of the adjusted natural frequency is weakened, and meanwhile, the frequency domain component amplitude feature corresponding to the abnormal data is amplified.
Optionally, when the dimensionless processing algorithm is a normalization algorithm, assuming that no abnormality occurs in the sensor in the device, the frequency-domain monitoring signal is:
wherein, due to f1(x) Is a period boxThe natural frequency is obvious, and because the high-frequency component is noise, the natural frequency is in the frequency band with lower frequency, and the frequency domain component amplitude marked with the natural frequency is afThen a isf>>ak(k≠f)。
Using a normalization algorithm, respectively pairing f1(x) Normalizing each level of frequency domain component to make the coefficient of each level of frequency domain component be 1, and recording the normalization parameter a adopted when the coefficient of each level of frequency domain component is 1k。
It should be noted that the normalization parameter akAnd k is 1,2, … …, that is, each level of frequency domain component has a corresponding normalization parameter.
After normalization, the frequency domain monitor signal is:
it can be seen that, with respect to the natural frequency, the ratio of the amplitude of the frequency domain component of the non-natural frequency to the amplitude of the frequency domain component of the natural frequency before normalization is
Due to af>>ak(k ≠ f), therefore, the frequency-domain component amplitude characteristics of the natural frequency are obvious, and after normalization, the frequency-domain component amplitude of the non-natural frequency and the frequency-domain component amplitude of the natural frequency become 1 (the amplitude of the natural frequency is 1), and the frequency-domain component amplitude characteristics of the natural frequency are weakened.
When the sensor in the device is abnormal, the frequency domain monitoring signal is assumed as follows:
wherein, bk≈ak，
Is the exception data.
Using a normalization algorithm, respectively pairing f2(x) Each level of the frequency domain components in (a) is divided by a corresponding normalization parameter akObtaining normalized frequency domain monitoring signals as follows:
wherein the content of the first and second substances,
relative to the natural frequency, before normalization, the ratio of the frequency domain component amplitude corresponding to the abnormal data to the frequency domain component amplitude of the natural frequency is
After normalization, the ratio of the frequency domain component amplitude corresponding to the abnormal data to the frequency domain component amplitude of the natural frequency becomes
(the amplitude of the natural frequency is 1). Due to af>>ak(k ≠ f), therefore has
Therefore, after normalization, the ratio of the frequency domain component amplitude of the abnormal data to the frequency domain component amplitude of the natural frequency is increased before adjustment, that is, the frequency domain component amplitude characteristic corresponding to the abnormal data is amplified.
And 104, inputting the frequency domain components of each level after amplitude adjustment in the frequency domain monitoring signals into a pre-trained anomaly detection model to obtain an anomaly detection result.
And the anomaly detection model learns the corresponding relation between each level of frequency domain component and the anomaly detection result which are adjusted by adopting a dimensionless processing algorithm.
Step 103 shows that when a sensor in the device is abnormal, the acquired time domain monitoring signal carries abnormal data, and according to a dimensionless processing algorithm, after the amplitudes of the frequency domain components at each level in the frequency domain monitoring signal are adjusted, the frequency domain component amplitude feature corresponding to the abnormal data is amplified, and at the same time, the frequency domain component amplitude feature of the natural frequency is weakened, so that the abnormal detection result can be obtained by inputting the frequency domain components at each level in the frequency domain monitoring signal after amplitude adjustment into a pre-trained abnormal detection model. Because the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result, the frequency domain monitoring signals are detected according to the anomaly detection model, and the efficiency and accuracy of result detection can be improved.
According to the equipment anomaly detection method, a single-dimensional time domain monitoring signal obtained by monitoring equipment is converted into a multi-dimensional frequency domain monitoring signal, more amplitude-frequency characteristics can be obtained, then the amplitude of each level of frequency domain component in the frequency domain monitoring signal is adjusted by using a dimensionless processing algorithm, the amplitude characteristics of the frequency domain component corresponding to the anomaly data can be amplified, finally the frequency domain monitoring signal is detected by using a pre-trained anomaly detection model, a detection result is obtained, and real-time and automatic anomaly detection of mass equipment can be realized. In addition, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result, so that the frequency domain monitoring signals are detected according to the anomaly detection model, and the efficiency and accuracy of result detection can be improved.
In the embodiment of the present invention, before the detection, the abnormality detection model may be trained in advance, and the above process will be described in detail with reference to fig. 3.
Fig. 3 is a flowchart illustrating a second method for detecting device abnormality according to an embodiment of the present invention.
As shown in fig. 3, before step 104, the device abnormality detection method may further include the steps of:
In the embodiment of the invention, in the operation process of the system monitoring equipment, the signal output by the sensor with the output signal of the periodic function in the equipment can be stored, so that the time domain historical signal monitored in the historical operation process of the equipment can be obtained when the abnormal detection model is trained.
In the embodiment of the invention, Fourier series expansion can be carried out on the time domain historical signal to obtain the frequency domain historical signal, and the frequency domain historical signal is the combination of infinite sine waves.
It can be understood that the single-dimensional time domain historical signal is converted into the multi-dimensional frequency domain historical signal, so that more amplitude-frequency characteristics can be obtained, and the accuracy of the detection result can be improved when the anomaly detection model is used for anomaly detection.
In the embodiment of the invention, after the amplitude of each level of frequency domain component in the frequency domain historical signal is adjusted according to the dimensionless processing algorithm, the frequency domain component amplitude characteristic of the natural frequency can be weakened, and the frequency domain component amplitude characteristic corresponding to the abnormal data is amplified. For example, the dimensionless processing algorithm may be a normalization algorithm, or the dimensionless processing algorithm may be any other algorithm that can weaken the frequency domain component amplitude feature of the natural frequency and amplify the frequency domain component amplitude feature corresponding to the abnormal data, which is not limited in this embodiment of the present invention.
In the embodiment of the invention, after the frequency domain historical signal is obtained, the amplitude of each level of frequency domain component in the frequency domain historical signal can be adjusted according to a dimensionless processing algorithm to obtain the adjusted frequency domain historical signal.
As a possible implementation manner, a normalization algorithm may be adopted to adjust the amplitudes of the frequency domain components at each level in the frequency domain historical signal, so that the amplitude features of the frequency domain components of the adjusted natural frequency are weakened, and the amplitude features of the frequency domain components corresponding to the abnormal data are amplified.
And 204, training an abnormality detection model according to the frequency domain components of each level after amplitude adjustment in the frequency domain historical signals and the historical operating state corresponding to the equipment.
The historical operating state comprises a normal state and an abnormal state.
Specifically, a positive sample for training the anomaly detection model is generated according to each level of frequency domain components after the adjustment of the historical signal amplitude corresponding to the normal state, and the positive sample is adopted to train the anomaly detection model.
In the embodiment of the invention, the anomaly detection model is trained according to the frequency domain components of all levels after amplitude adjustment in the frequency domain historical signals and the corresponding historical operating state of the equipment, so that the anomaly detection model can learn to obtain the corresponding relation between the frequency domain components of all levels after adjustment by adopting a dimensionless processing algorithm and the anomaly detection result, and the efficiency and the accuracy of result detection can be improved when the frequency domain monitoring signals are detected according to the anomaly detection model.
As a possible implementation manner, a regression prediction manner may be adopted to train an anomaly detection model using a Long Short Term Memory (LSTM) neural network, so that the anomaly detection model using the LSTM neural network learns to obtain a corresponding relationship between frequency domain components of each level and an anomaly detection result adjusted by a dimensionless processing algorithm.
In the device anomaly detection method of the embodiment, a single-dimensional time domain historical signal is converted into a multi-dimensional frequency domain historical signal, so that more amplitude-frequency characteristics can be obtained, and the accuracy of a detection result can be improved when an anomaly detection model is used for anomaly detection. According to a dimensionless processing algorithm, the amplitude of each level of frequency domain component in the frequency domain historical signal is adjusted, the amplitude characteristic of the frequency domain component corresponding to abnormal data can be amplified, and finally, according to each level of frequency domain component after the amplitude adjustment in the frequency domain historical signal and the historical running state corresponding to the equipment, an abnormality detection model is trained, so that the abnormality detection model can learn to obtain the corresponding relation between each level of frequency domain component after the adjustment by the dimensionless processing algorithm and an abnormality detection result, and when the frequency domain monitoring signal is detected according to the abnormality detection model, the efficiency and accuracy of result detection can be improved.
In order to implement the above embodiments, the present invention further provides an apparatus anomaly detection device.
Fig. 4 is a schematic structural diagram of an apparatus anomaly detection device according to an embodiment of the present invention.
As shown in fig. 4, the device abnormality detection apparatus 400 includes: an acquisition module 410, a transformation module 420, an adjustment module 430, and a detection module 440. Wherein the content of the first and second substances,
the obtaining module 410 is configured to obtain a time-domain monitoring signal obtained by monitoring a device.
And the transforming module 420 is configured to perform frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal.
As a possible implementation manner, the transform module 420 is specifically configured to perform fourier series expansion on the time domain monitoring signal to obtain a frequency domain monitoring signal
Where k is the frequency domain component
Number of stages of (A)kIs the amplitude.
And an adjusting module 430, configured to adjust amplitudes of frequency domain components of each level in the frequency domain monitoring signal according to a dimensionless processing algorithm.
As a possible implementation manner, the adjusting module 430 is specifically configured to adjust the amplitude of each level of the frequency domain component in the frequency domain monitoring signal by using a normalization algorithm.
The detection module 440 is configured to input the frequency domain components of each level, whose amplitudes are adjusted, in the frequency domain monitoring signal into a pre-trained anomaly detection model to obtain an anomaly detection result; and the anomaly detection model learns the corresponding relation between each level of frequency domain component and the anomaly detection result which are adjusted by adopting a dimensionless processing algorithm.
Further, in a possible implementation manner of the embodiment of the present invention, referring to fig. 5, on the basis of the embodiment shown in fig. 4, the apparatus abnormality detection apparatus 400 may further include: a filtering denoising module 450 and a training module 460.
The screening and denoising module 450 is configured to screen frequency domain components of each level of the frequency domain monitoring signal after performing frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal, and retain the frequency domain components of a preset level; and/or denoising each level of frequency domain components of the frequency domain monitoring signal to filter out frequency domain components with frequencies higher than a preset threshold value.
A training module 460, configured to obtain a time-domain historical signal monitored in a historical operation process of the device; carrying out frequency domain transformation on the time domain historical signal to obtain a frequency domain historical signal; adjusting the amplitude of each level of frequency domain component in the frequency domain historical signal according to a dimensionless processing algorithm; training an anomaly detection model according to each level of frequency domain components after amplitude adjustment in the frequency domain historical signals and the historical operating state corresponding to the equipment; the historical operating state includes a normal state and an abnormal state.
It should be noted that the foregoing explanation on the device anomaly detection method embodiment is also applicable to the device anomaly detection apparatus 400 of this embodiment, and is not repeated here.
The device anomaly detection device of the embodiment converts a single-dimensional time domain monitoring signal obtained by monitoring the device into a multi-dimensional frequency domain monitoring signal, so that more amplitude-frequency characteristics can be obtained, then adjusts the amplitude of each level of frequency domain component in the frequency domain monitoring signal by using a dimensionless processing algorithm, can amplify the amplitude characteristic of the frequency domain component corresponding to the anomaly data, finally detects the frequency domain monitoring signal by using a pre-trained anomaly detection model to obtain a detection result, and can realize real-time and automatic anomaly detection on mass devices. In addition, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result, so that the frequency domain monitoring signals are detected according to the anomaly detection model, and the efficiency and accuracy of result detection can be improved.
In order to implement the foregoing embodiment, the present invention further provides a computer device, including: a memory and a processor, wherein the processor runs a program corresponding to an executable program code stored in the memory by reading the executable program code for executing the device abnormality detection method according to the foregoing embodiment.
For clarity of explanation of the specific architecture of the aforementioned computer device, FIG. 6 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 6 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 6, computer device 12 is in the form of a general purpose computer device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., local area networks, wide area networks, and/or public networks such as the Internet) through network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive arrays, Redundant Array of Independent Disks (RAID) systems, tape drives, and data backup storage systems, to name a few.
The processing unit 16 implements the above-described device abnormality detection method by executing a program stored in the system memory 28 to thereby execute various functional applications and data processing.
To achieve the above object, the present invention further provides a computer program product, wherein when instructions in the computer program product are executed by a processor, the method for detecting device anomalies according to the foregoing embodiments is performed.
In order to implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, is capable of implementing the device abnormality detection method as described in the foregoing embodiments.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. An apparatus abnormality detection method, characterized in that the method comprises the steps of:
acquiring a time domain monitoring signal obtained by monitoring equipment;
carrying out frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal;
adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal according to a dimensionless processing algorithm;
inputting each level of frequency domain components with the adjusted amplitudes in the frequency domain monitoring signals into a pre-trained anomaly detection model to obtain anomaly detection results; wherein, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result;
wherein, after the frequency domain transformation is performed on the time domain monitoring signal to obtain the frequency domain monitoring signal, the method further comprises:
screening all levels of frequency domain components of the frequency domain monitoring signal, and reserving frequency domain components of preset levels;
and/or denoising each level of frequency domain components of the frequency domain monitoring signal to filter out frequency domain components with frequencies higher than a preset threshold value.
2. The method for detecting device anomalies according to claim 1, wherein the adjusting the amplitudes of the frequency-domain components of each level in the frequency-domain monitoring signal according to a dimensionless processing algorithm includes:
and adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal by adopting a normalization algorithm.
3. The method according to claim 1, wherein before inputting the frequency domain components of each level, which have been amplitude-adjusted, in the frequency domain monitoring signal into a pre-trained anomaly detection model to obtain an anomaly detection result, the method further comprises:
acquiring a time domain historical signal monitored in the historical operation process of equipment;
performing frequency domain transformation on the time domain historical signal to obtain a frequency domain historical signal;
adjusting the amplitude of each level of frequency domain component in the frequency domain historical signal according to the dimensionless processing algorithm;
training the abnormal detection model according to each level of frequency domain components after amplitude adjustment in the frequency domain historical signal and the historical operating state corresponding to the equipment; the historical operating state comprises a normal state and an abnormal state.
4. The device abnormality detection method according to claim 3, wherein said training of said abnormality detection model includes:
and training an abnormality detection model adopting an LSTM neural network by adopting a regression prediction mode.
5. The apparatus anomaly detection method according to claim 1, wherein said frequency-domain transforming the time-domain monitor signal to obtain a frequency-domain monitor signal comprises:
fourier series expansion is carried out on the time domain monitoring signal to obtain a frequency domain monitoring signal
Where k is the frequency domain component
Number of stages of (A)kIs the amplitude.
6. An apparatus for detecting abnormality of a device, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a time domain monitoring signal obtained by monitoring equipment;
the transformation module is used for carrying out frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal;
the adjusting module is used for adjusting the amplitude of each level of frequency domain component in the frequency domain monitoring signal according to a dimensionless processing algorithm;
the detection module is used for inputting each level of frequency domain components with the adjusted amplitudes in the frequency domain monitoring signals into a pre-trained anomaly detection model to obtain anomaly detection results; wherein, the anomaly detection model learns the corresponding relation between each level of frequency domain components adjusted by the dimensionless processing algorithm and the anomaly detection result;
the screening and denoising module is used for screening each level of frequency domain components of the frequency domain monitoring signal after performing frequency domain transformation on the time domain monitoring signal to obtain a frequency domain monitoring signal, and reserving frequency domain components of a preset level; and/or denoising each level of frequency domain components of the frequency domain monitoring signal to filter out frequency domain components with frequencies higher than a preset threshold value.
7. A computer device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the device anomaly detection method according to any one of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the device abnormality detection method according to any one of claims 1 to 5.
9. A computer program product, wherein instructions, when executed by a processor, perform the device anomaly detection method of any one of claims 1-5.
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