EP4702407A1

EP4702407A1 - Fault diagnosis method and apparatus for computer numeric controlled machine tool

Info

Publication number: EP4702407A1
Application number: EP23938884.6A
Authority: EP
Inventors: Bin Zhang; Armin Roux
Original assignee: Siemens AG; Siemens Corp
Current assignee: Siemens AG; Siemens Corp
Priority date: 2023-05-31
Filing date: 2023-05-31
Publication date: 2026-03-04
Also published as: CN120898183A; WO2024243893A1

Abstract

A fault diagnosis method(100) for a computer numeric controlled machine tool(20). The method includes: acquiring real-time operation data of the computer numeric controlled machine tool(110); calling a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using a hidden Markov model (HMM), so as to obtain a current working condition of the computer numeric controlled machine tool(20), the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool(120); and determining whether the current working condition of the computer numeric controlled machine tool(20) is the known working condition, if the current working condition is the known working condition, calling fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a generation adversarial network (GAN) model, if the current working condition is an unknown working condition, establishing a new working condition type, fusing the new working condition type into the fault diagnosis model, and calling the fused fault diagnosis model for fault diagnosis using the GAN model(130).

Description

FAULT DIAGNOSIS METHOD AND APPARATUS FOR COMPUTER NUMERIC CONTROLLED MACHINE TOOL

Technical Field

The present invention mainly relates to the field of industrial digitalization, and in particular, to a fault diagnosis method and apparatus for a computer numeric controlled machine tool.

Background

In a modern digital intelligent factory, a computer numeric controlled machine tool is a very important manufacturing device. Faults produced in use will reduce production efficiency and increase production costs. Therefore, it is necessary to timely perform fault diagnosis on the computer numeric controlled machine tool. A large amount of data will be accumulated during the operation of the computer numeric controlled machine tool. The data contains a large amount of health state information. The current fault diagnosis methods collect and analyze the data in order to obtain fault diagnosis results. However, with the operation of the computer numeric controlled machine tool, the recognition rate of fault diagnosis will be reduced, and machine tool operation data belongs to industrial time series data. The problem of imbalance between normal samples and fault samples is caused by the industrial time series data, and the fault diagnosis will be inaccurate.

Summary

In order to solve the above technical problem, the present invention provides a fault diagnosis method and apparatus for a computer numeric controlled machine tool, so as to improve the recognition rate and accuracy of fault diagnosis.
To achieve the above object, the present invention provides a fault diagnosis method for a computer numeric controlled machine tool. The method includes: acquiring real-time operation data of the computer numeric controlled machine tool; calling a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using a hidden Markov model (HMM) , so as to obtain a current working condition of the computer numeric controlled machine tool, the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool; and determining whether the current working condition of the computer numeric controlled machine tool is the known working condition, if the current working condition is the known working condition, calling fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a generation adversarial network (GAN) model, if the current working condition is an unknown working condition, establishing a new working condition type, fusing the new working condition type into the fault diagnosis model, and calling the fused fault diagnosis model for fault diagnosis using the GAN model. Therefore, the HMM is used for pattern recognition, and the current working condition of the computer numeric controlled machine tool is obtained according to the real-time operation data of the computer numeric controlled machine tool. In consideration of data features under different working conditions, the problem that the change range of state data under different working conditions is greater than fault features is solved. Moreover, deep information of multi-source fusion data may be learned through the GAN model, and faults may be accurately classified. Furthermore, an adversarial learning mechanism in the GAN model may solve the problem of imbalance between normal samples and fault samples.
Optionally, the operation of calling a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM includes: training the HMM using three types of state data: rotating speed, current, and load, and calling the feature dataset of the fault diagnosis model to extract the state features of the real-time operation data using the HMM. Therefore, a multi-dimensional dataset is established using three types of state data: rotating speed, current, and load, and the HMM is trained according to the multi-dimensional dataset. By classifying data under different working conditions and establishing models for multi-source data respectively, the recognition ability of the models to the different working conditions is improved. Moreover, during the extraction of the state features, the multi-source data may improve the fault recognition rate.
Optionally, after acquiring real-time operation data of the computer numeric controlled machine tool, the method further includes: performing data preprocessing on the real-time operation data, the data preprocessing including outlier removal, data resampling, and data fitting. Therefore, outlier data and duplicated data in the real-time operation data may be removed, thereby reducing the amount of subsequent data processing, improving the accuracy of data sources, and then improving the accuracy of fault diagnosis.
Optionally, the operation of performing data preprocessing on the real-time operation data includes: processing the historical data of the known working condition to obtain an original dataset, and calling the original dataset to perform data preprocessing on the real-time operation data. Therefore, the original dataset includes data information of the known working condition. By calling the original dataset to perform data preprocessing on the real-time operation data, the real-time operation data may be aligned with the historical data of the known working condition, thereby improving the efficiency of data processing.
Optionally, the method further includes: establishing a diagnosis result dataset according to a fault diagnosis result, and updating the fault diagnosis model using the diagnosis result dataset. Therefore, the fault diagnosis model is updated using the diagnosis result dataset, thereby realizing the feedback and update of the fault diagnosis model, improving the accuracy of the fault diagnosis model, and then improving the accuracy of fault diagnosis.
The present invention further provides a fault diagnosis apparatus for a computer numeric controlled machine tool. The apparatus includes: an acquisition module, configured to acquire real-time operation data of the computer numeric controlled machine tool; a determination module, configured to call a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM, so as to obtain a current working condition of the computer numeric controlled machine tool, the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool; and a diagnosis module, configured to determine whether the current working condition of the computer numeric controlled machine tool is the known working condition, if the current working condition is the known working condition, call fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a GAN model, if the current working condition is an unknown working condition, establish a new working condition type, fuse the new working condition type into the fault diagnosis model, and call the fused fault diagnosis model for fault diagnosis using the GAN model.
Optionally, the operation of calling, by the determination module, a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM includes: training the HMM using three types of state data: rotating speed, current, and load, and calling the feature dataset of the fault diagnosis model to extract the state features of the real-time operation data using the HMM.
Optionally, after the acquisition module acquires real-time operation data of the computer numeric controlled machine tool, data preprocessing is performed on the real-time operation data, the data preprocessing including outlier removal, data resampling, and data fitting.
Optionally, the operation of performing, by the acquisition module, data preprocessing on the real-time operation data includes: processing the historical data of the known working condition to obtain an original dataset, and calling the original dataset to perform data preprocessing on the real-time operation data.
Optionally, the apparatus is further configured to: establish a diagnosis result dataset according to a fault diagnosis result, and update the fault diagnosis model using the diagnosis result dataset.
The present invention further provides an electronic device, including a processor, a memory and instructions stored in the memory. The instructions, when executed by the processor, implement the method as described above.
The present invention further provides a computer-readable storage medium, having computer instructions stored thereon. The computer instructions, when executed, perform the method as described above.
The present invention further provides a computer program product, including a computer program which, when executed by a processor, implements the method as described above.

Brief Description of Drawings

The following figures are only intended to give schematic illustrations and explanations of the present invention but are not intended to limit a scope of the present invention.
FIG. 1 is a flowchart of a fault diagnosis method according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a fault diagnosis method according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of data acquisition and preprocessing of a computer numeric controlled machine tool according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a fault diagnosis apparatus according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Descriptions of Reference Numerals:
100 Fault diagnosis method
110-130 Steps
20 Computer numeric controlled machine tool
201-208 Steps
21 Fault diagnosis model
211-215 Steps
221 Original dataset
222 Feature dataset
223 Diagnosis result dataset
311 Computer numeric controlled machine tool
312 Vibration sensor
313 Constant current adapter
314 Data acquisition card
315 Computer numeric control system
316 Data bus
317 OPC UA protocol
321 Original vibration signal
322 Signal demodulation and frequency domain analysis unit
331 Current
332 Temperature
333 Load
334 Preprocessing unit
400 Fault diagnosis apparatus
410 Acquisition module
420 Determination module
430 Diagnosis module
500 Electronic device
510 Processor
520 Memory

Detailed Description

To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific implementations of the present invention are described with reference to the accompanying drawings.
Many specific details are set forth in the following description to facilitate a full understanding of this application, but this application may alternatively be implemented in other manners different from those described herein and is therefore not limited by specific embodiments disclosed below.
As shown in this application and the claims, words such as “a/an, ” “one, ” “one kind, ” and/or “the” do not refer specifically to singular forms and may also include plural forms, unless the context expressly indicates an exception. In general, terms "comprise" and "include" merely indicate including clearly identified steps and elements. The steps and elements do not constitute an exclusive list. A method or a device may also include other steps or elements.
The present invention provides a fault diagnosis method for a computer numeric controlled machine tool. FIG. 1 is a flowchart of a fault diagnosis method 100 according to an embodiment of the present invention. As shown in FIG. 1, the method 100 includes the following steps:
Step 110: Obtain real-time operation data of a computer numeric controlled machine tool.
Various types of sensors are installed in the computer numeric controlled machine tool, and the real-time operation data of the computer numeric controlled machine tool may be acquired by the sensors. The real-time operation data may include real-time current, real-time temperature, real-time load, etc. For example, the real-time current of the computer numeric controlled machine tool may be acquired by a galvanometer, the real-time temperature of the computer numeric controlled machine tool may be acquired by a temperature sensor, and the real-time load may be acquired by a force sensor. It may be understood that the real-time operation data of the computer numeric controlled machine tool is industrial time series data, which is a data sequence composed of data at different times. The real-time operation data may be original data. For example, an acceleration of a cutter head of the computer numeric controlled machine tool is acquired by an acceleration sensor. The real-time operation data may also be processing data. For example, after the acceleration of the cutter head of the computer numeric controlled machine tool is acquired by the acceleration sensor, the acceleration is integrated once to acquire a speed of the cutter head of the computer numeric controlled machine tool, and the acceleration is integrated twice to acquire a displacement of the cutter head of the computer numeric controlled machine tool.
In some embodiments, after acquiring real-time operation data of the computer numeric controlled machine tool, the method further includes: performing data preprocessing on the real-time operation data, the data preprocessing including outlier removal, data resampling, and data fitting. Specifically, after acquiring the real-time operation data of the computer numeric controlled machine tool, data is preprocessed, including outlier removal, data resampling, and data fitting. Therefore, outlier data and duplicated data in the real-time operation data may be removed, thereby reducing the amount of subsequent data processing, improving the accuracy of data sources, and then improving the accuracy of fault diagnosis.
In some embodiments, the operation of performing data preprocessing on the real-time operation data includes: processing the historical data of the known working condition to obtain an original dataset, and calling the original dataset to perform data preprocessing on the real-time operation data. Specifically, the historical data of the known working condition is processed using a data processing algorithm to obtain the original dataset. The original dataset includes data information of the known working condition. By calling the original dataset to perform data preprocessing on the real-time operation data, the real-time operation data may be aligned with the historical data of the known working condition, thereby improving the efficiency of data processing.
Step 120: Call a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM, so as to obtain a current working condition of the computer numeric controlled machine tool, the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool.
The HMM (hidden Markov model) is a statistical model, which may give an observation probability of each state. The HMM may be trained using the historical data of the known working condition of the computer numeric controlled machine tool. The real-time operation data is inputted into the HMM, the HMM may call the feature dataset in the fault diagnosis model to extract the state features of the real-time operation data, and the current working condition of the computer numeric controlled machine tool is outputted. The HMM is used for pattern recognition, and the current working condition of the computer numeric controlled machine tool is obtained according to the real-time operation data of the computer numeric controlled machine tool. In consideration of data features under different working conditions, the problem that the change range of state data under different working conditions is greater than fault features is solved.
In some embodiments, the operation of calling a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM includes: training the HMM using three types of state data: rotating speed, current, and load, and calling the feature dataset of the fault diagnosis model to extract the state features of the real-time operation data using the HMM. Specifically, a multi-dimensional dataset is established using three types of state data: rotating speed, current, and load, and the HMM is trained according to the multi-dimensional dataset. By classifying data under different working conditions and establishing models for multi-source data respectively, the recognition ability of the models to the different working conditions is improved. Moreover, during the extraction of the state features, the multi-source data may improve the fault recognition rate.
FIG. 3 is a schematic diagram of data acquisition and preprocessing of a computer numeric controlled machine tool according to an embodiment of the present invention. As shown in FIG. 3, a computer numeric controlled machine tool 311 is equipped with a vibration sensor 312, a constant current adapter 313, and a data acquisition card 314. An original vibration signal 321 of a cutter head may be acquired. A signal demodulation and frequency domain analysis unit 322 performs demodulation and frequency domain analysis on the original vibration signal 321 to obtain frequency domain features of a vibration signal. The computer numeric controlled machine tool 311 is further connected to a computer numeric control system 315. The computer numeric control system 315 sends a current 331, a temperature 332, and a load 333 to a computing device such as an upper computer through a data bus 316 and an OPC UA protocol 317. In the preprocessing, the current 331, the temperature 332, and the load 333 are preprocessed such as outlier removal and resampling.
Step 130: Determine whether the current working condition of the computer numeric controlled machine tool is the known working condition, if the current working condition is the known working condition, call fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a GAN model, if the current working condition is an unknown working condition, establish a new working condition type, fuse the new working condition type into the fault diagnosis model, and call the fused fault diagnosis model for fault diagnosis using the GAN model.
Various working conditions may be defined according to a shaft feed speed, a spindle speed, a coolant, an ambient temperature, a cutting workpiece, and a cutting mode of the computer numeric controlled machine tool. For example, in the first working condition, the shaft feed speed is 20,000 mm/min, the spindle speed is 8,000 r/min, there is the coolant, the ambient temperature is 10-20℃, the cutting workpiece is steel, and the cutting mode is air cutting. In the second working condition, the shaft feed speed is 10,000 mm/min, the spindle speed is 3,000 r/min, there is the coolant, the ambient temperature is 0-10℃, the cutting workpiece is steel, and the cutting mode is face milling. In the third working condition, the shaft feed speed is 500 mm/min, the spindle speed is 5,000 r/min, there is no coolant, the ambient temperature is 20-30℃, the cutting workpiece is steel, and the cutting mode is end milling. The current working condition of the computer numeric controlled machine tool is compared with a predefined working condition. If the current working condition is a known working condition, fault diagnosis model parameters of the known working condition in the fault diagnosis model are called using the GAN model. The GAN model is a deep learning model, and an ideal output is generated by the mutual game between a generation model and a determination model. Deep information of multi-source fusion data may be learned through the GAN model, and faults may be accurately classified. Furthermore, an adversarial learning mechanism in the GAN model may solve the problem of imbalance between normal samples and fault samples. If the current working condition is an unknown working condition, a new working condition type is established, and the new working condition type is fused into the fault diagnosis model. That is, the new working condition type is matched with the known working condition type, training is performed based on model parameters of the known working condition type to obtain model parameters of the new working condition type, and the model parameters of the new working condition type in the fused fault diagnosis model are called for fault diagnosis using the GAN model. For example, the faults of the computer numeric controlled machine tool are diagnosed as host down, weak current, strong current, etc.
In some embodiments, the method further includes: establishing a diagnosis result dataset according to a fault diagnosis result, and updating the fault diagnosis model using the diagnosis result dataset. Specifically, the diagnosis result dataset is established according to the fault diagnosis result, and the fault diagnosis model is updated using the diagnosis result dataset, thereby realizing the feedback and update of the fault diagnosis model, improving the accuracy of the fault diagnosis model, and then improving the accuracy of fault diagnosis.
FIG. 2 is a schematic diagram of a fault diagnosis method according to an embodiment of the present invention. An exemplary description of the fault diagnosis method in the embodiment of the present invention will be given below with reference to FIG. 2.
Real-time Fault Diagnosis
Step 201: Acquire real-time operation data of a computer numeric controlled machine tool 20.
Step 202: Call an original dataset 221 to preprocess the real-time operation data.
Step 203: Call a feature dataset 222 to extract state features using an HMM.
Step 204: Determine whether a working condition is known, if yes, perform step 205, and if no, perform step 206.
Step 205: Call model parameters of the known working condition for fault diagnosis using a GAN model.
Step 206: Establish a new working condition type.
Step 207: Fuse the new working condition type into a fault diagnosis model to obtain model parameters of the new working condition type.
Step 208: Output a diagnosis result, and establish a diagnosis result dataset 225.
Model Construction
Step 211: Acquire historical data of the known working condition from the computer numeric controlled machine tool 20.
Step 212: Process the historical data of the known working condition using a data processing algorithm to obtain the original dataset 221.
Step 213: Extract features using a feature extraction algorithm (HMM transform) to obtain the feature dataset 222.
Step 214: Perform model training using the feature dataset.
Step 215: Output the fault diagnosis model.
The embodiment of the present invention provides a fault diagnosis method for a computer numeric controlled machine tool. An HMM is used for pattern recognition, and a current working condition of the computer numeric controlled machine tool is obtained according to real-time operation data of the computer numeric controlled machine tool. In consideration of data features under different working conditions, the problem that the change range of state data under different working conditions is greater than fault features is solved. Moreover, deep information of multi-source fusion data may be learned through a GAN model, and faults may be accurately classified. Furthermore, an adversarial learning mechanism in the GAN model may solve the problem of imbalance between normal samples and fault samples.
The present invention further provides a fault diagnosis apparatus. FIG. 4 is a schematic diagram of a fault diagnosis apparatus 400 according to an embodiment of the present invention. As shown in FIG. 4, the fault diagnosis apparatus 400 includes:
an acquisition module 410, configured to acquire real-time operation data of the computer numeric controlled machine tool;
a determination module 420, configured to call a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM, so as to obtain a current working condition of the computer numeric controlled machine tool, the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool; and
a diagnosis module 430, configured to determine whether the current working condition of the computer numeric controlled machine tool is the known working condition, if the current working condition is the known working condition, call fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a GAN model, if the current working condition is an unknown working condition, establish a new working condition type, fuse the new working condition type into the fault diagnosis model, and call the fused fault diagnosis model for fault diagnosis using the GAN model.
In some embodiments, the operation of calling, by the determination module 420, a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM includes: training the HMM using three types of state data: rotating speed, current, and load, and calling the feature dataset of the fault diagnosis model to extract the state features of the real-time operation data using the HMM.
In some embodiments, after the acquisition module 410 acquires real-time operation data of the computer numeric controlled machine tool, data preprocessing is performed on the real-time operation data, the data preprocessing including outlier removal, data resampling, and data fitting.
In some embodiments, the operation of performing, by the acquisition module 410, data preprocessing on the real-time operation data includes: processing the historical data of the known working condition to obtain an original dataset, and calling the original dataset to perform data preprocessing on the real-time operation data.
In some embodiments, the apparatus 400 is further configured to: establish a diagnosis result dataset according to a fault diagnosis result, and update the fault diagnosis model using the diagnosis result dataset.
The present invention further provides an electronic device 500. FIG. 5 is a schematic diagram of an electronic device 500 according to an embodiment of the present invention. As shown in FIG. 5, the electronic device 500 includes a processor 510 and a memory 520. The memory 520 stores instructions. The instructions, when executed by the processor 510, implement the method 100 as described above.
The present invention further provides a computer-readable storage medium, having computer instructions stored thereon. The computer instructions, when executed, implement the method 100 as described above.
The present invention further provides a computer program product, including a computer program. The computer program, when executed by a processor, implements the method 100 described above.
Some aspects of the method and apparatus of the present invention may be entirely executed by hardware, may be entirely executed by software (including firmware, resident software, microcode, and the like) , or may be executed by a combination of hardware and software. The above hardware or software may be referred to as "data block" , "module" , "engine" , "unit" , "component" , or "system" . A processor may be one or more application specific integrated circuits (ASIC) , digital signal processors (DSP) , digital signal processing devices (DSPD) , programmable logic devices (PLC) , field programmable gate arrays (FPGA) , processors, controllers, microcontrollers, microprocessors, or a combination thereof. In addition, various aspects of the present invention may be embodied as computer products located in one or more computer-readable media, the product including a computer-readable program code. For example, the computer-readable medium may include, but is not limited to, a magnetic storage device (for example, a hard disk, a floppy disk, a magnetic tape ... ) , an optical disk (for example, a compact disk (CD) , a digital versatile disk (DVD) , ... ) , a smart card, and a flash memory device (for example, a card, a stick, a key driver, ... ) .
The flowchart is used to be configured to describe operations performed by the method according to the embodiment of this application herein. It is to be understood that the foregoing operations may not be performed accurately according to the order. On the contrary, the operations may be performed in a reverse order or simultaneously. At the same time, or other operations are added into these processes, or one or multiple operations are removed from these processes.
It is to be understood that, although this specification is described according to each embodiment, each embodiment may not include only one independent technical solution. The description manner of this specification is merely for clarity. This specification is to be considered as a whole by a person skilled in the art, and the technical solution in each embodiment may also be properly combined, to form other implementations that may be understood by a person skilled in the art.
The foregoing is merely specific schematic implementations of the present invention, and is not intended to limit the scope of the present invention. Any equivalent change, modification, and combination made by a person skilled in the art without departing from the conception and principles of the present invention shall all fall within the protection scope of the present invention.
Independent of the grammatical term usage, individuals with male, female or other gender identities are included within the term.

Claims

A fault diagnosis method (100) for a computer numeric controlled machine tool, the method (100) comprising:

acquiring real-time operation data of the computer numeric controlled machine tool (110) ;

calling a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using a hidden Markov model (HMM) , so as to obtain a current working condition of the computer numeric controlled machine tool, the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool (120) ; and

determining whether the current working condition of the computer numeric controlled machine tool is the known working condition, if the current working condition is the known working condition, calling fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a generation adversarial network (GAN) model, if the current working condition is an unknown working condition, establishing a new working condition type, fusing the new working condition type into the fault diagnosis model, and calling the fused fault diagnosis model for fault diagnosis using the GAN model (130) .
The fault diagnosis method (100) according to claim 1, wherein the calling a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM comprises: training the HMM using three types of state data: rotating speed, current, and load, and calling the feature dataset of the fault diagnosis model to extract the state features of the real-time operation data using the HMM.
The fault diagnosis method (100) according to claim 1 or 2, wherein after acquiring real-time operation data of the computer numeric controlled machine tool, the method further comprises: performing data preprocessing on the real-time operation data, the data preprocessing comprising outlier removal, data resampling, and data fitting.
The fault diagnosis method (100) according to claim 3, wherein the performing data preprocessing on the real-time operation data comprises: processing the historical data of the known working condition to obtain an original dataset, and calling the original dataset to perform data preprocessing on the real-time operation data.
The fault diagnosis method (100) according to claim 1, the method (100) further comprising: establishing a diagnosis result dataset according to a fault diagnosis result, and updating the fault diagnosis model using the diagnosis result dataset.
A fault diagnosis apparatus (400) for a computer numeric controlled machine tool, the apparatus (400) comprising:

an acquisition module (410) , configured to acquire real-time operation data of the computer numeric controlled machine tool;

a determination module (420) , configured to call a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using a hidden Markov model (HMM) , so as to obtain a current working condition of the computer numeric controlled machine tool, the fault diagnosis model being trained by historical data of a known working condition of the computer numeric controlled machine tool; and

a diagnosis module (430) , configured to determine whether the current working condition of the computer numeric controlled machine tool is the known working condition, if the current working condition is the known working condition, call fault diagnosis model parameters of the known working condition in the fault diagnosis model for fault diagnosis using a generation adversarial network (GAN) model, if the current working condition is an unknown working condition, establish a new working condition type, fuse the new working condition type into the fault diagnosis model, and call the fused fault diagnosis model for fault diagnosis using the GAN model.
The fault diagnosis apparatus (400) according to claim 6, wherein the operation of calling, by the determination module (420) , a feature dataset of a fault diagnosis model to extract state features of the real-time operation data using an HMM comprises: training the HMM using three types of state data: rotating speed, current, and load, and calling the feature dataset of the fault diagnosis model to extract the state features of the real-time operation data using the HMM.
The fault diagnosis apparatus (400) according to claim 6 or 7, wherein after the acquisition module (410) acquires real-time operation data of the computer numeric controlled machine tool, data preprocessing is performed on the real-time operation data, the data preprocessing comprising outlier removal, data resampling, and data fitting.
The fault diagnosis apparatus (400) according to claim 8, wherein the operation of performing, by the acquisition module (410) , data preprocessing on the real-time operation data comprises: processing the historical data of the known working condition to obtain an original dataset, and calling the original dataset to perform data preprocessing on the real-time operation data.
The fault diagnosis apparatus (400) according to claim 6, the apparatus (400) being further configured to: establish a diagnosis result dataset according to a fault diagnosis result, and update the fault diagnosis model using the diagnosis result dataset.
An electronic device (500) , comprising a processor (510) , a memory (520) , and instructions stored in the memory (520) , wherein the instructions, when executed by the processor (510) , implement the method (100) according to any one of claims 1-5.
A computer-readable storage medium, having computer instructions stored thereon, wherein the computer instructions, when executed, perform the method (100) according to any one of claims 1-5.
A computer program product, comprising a computer program which, when executed by a processor, implements the method (100) according to any one of claims 1-5.