Steam turbine generator state detection method based on vibration signals and related device

Abstract

The invention discloses a steam turbine generator state detection method based on a vibration signal and a related device, and relates to the technical field of generator fault detection, and the method comprises the following steps: collecting vibration information data of a steam turbine generator, and carrying out the preprocessing; performing insulation fatigue calculation according to the preprocessed vibration information data to obtain an insulation fatigue coefficient; and performing grading evaluation on the state of the steam turbine generator according to the insulation fatigue coefficient to obtain a state detection result. The problem that in the prior art, measurement needs to be carried out under shutdown or specific stable working conditions, and continuous online monitoring is difficult to achieve can be solved.

Description

Technical Field

[0001] This invention relates to the field of generator fault detection technology, specifically to a method and related apparatus for detecting the condition of a steam turbine generator based on vibration signals. Background Technology

[0002] As a core component of the power system, the safe and stable operation of the generator is crucial to the entire system. Vibration, to a certain extent, reflects the generator's operating characteristics and allows for the inference of its operating status. Generator vibration sources can be broadly categorized into electromagnetic and mechanical types, but these two are often coupled. Shaft misalignment, strong radial unbalanced magnetic pull generated by the air gap magnetic field between the stator and rotor, and unbalanced magnetic pull caused by short circuits or loosening of the stator winding turns leading to local magnetic field distortion can all cause generator vibration. Excessive vibration exacerbates wear on electrical components, reduces their insulation performance, and in severe cases, can lead to unit outages. The monitoring and diagnosis of generator vibration, from initial experience-based diagnosis to current intelligent diagnosis, revolves around capturing, separating, and identifying vibration characteristics. Currently, the main methods for detecting and analyzing generator vibration include time-domain / frequency-domain signal analysis, modal and equilibrium analysis, and torsional vibration analysis. These methods typically rely on specialized signal processing equipment and systems, extracting characteristic frequencies, amplitudes, and phases from the vibration signal and combining this information with unit operating parameters for status assessment and anomaly identification. This has become a widely adopted technique in this field.

[0003] However, the aforementioned existing methods still have several limitations in practical applications. First, the reliance on high-precision synchronous speed signals increases system complexity and dependence on additional speed measuring devices. Second, complex sensor arrays need to be installed at key locations on or inside the generator casing, which is cumbersome and may affect the normal operation of the unit. Therefore, measurements need to be taken under shutdown or specific stable operating conditions, making continuous online monitoring difficult. In addition, several diagnostic models rely on accurate physical or mathematical models of the generator, which are difficult to model and susceptible to individual differences and aging of the unit, leading to a decrease in generalization ability and practicality. Summary of the Invention

[0004] The purpose of this invention is to provide a method and related device for detecting the condition of a steam turbine generator based on vibration signals, so as to solve the problem that it is difficult to achieve continuous online monitoring because measurements need to be taken under shutdown or specific stable operating conditions.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a method for detecting the condition of a steam turbine generator based on vibration signals includes the following steps: Vibration data of the steam turbine generator are collected and preprocessed. Insulation fatigue calculations are performed based on the preprocessed vibration information data to obtain the insulation fatigue coefficient; The condition of the steam turbine generator is graded and evaluated based on the insulation fatigue coefficient to obtain the condition detection results.

[0006] In some implementations, vibration information data of the steam turbine generator is collected and preprocessed, specifically including: The vibration information data is subjected to consistency verification and per-unit processing to obtain preprocessed vibration information data.

[0007] In some implementations, insulation fatigue calculations are performed based on preprocessed vibration information data to obtain the insulation fatigue coefficient, specifically including: Calculate kurtosis and harmonic distortion based on the preprocessed vibration information data; Calculate the variable insulation fatigue value based on the kurtosis and harmonic distortion rate; The insulation fatigue coefficient is calculated based on the variable insulation fatigue value and the preset fixed insulation fatigue value.

[0008] In some implementations, kurtosis and harmonic distortion rates are calculated based on preprocessed vibration information data using the following formula:

[0009]

[0010] in, For ravine, For the first Each vibration information data value, The average value of the vibration information data. The total amount of vibration information data, Harmonic distortion rate, The amplitude of the highest order harmonic. The fundamental amplitude, The highest order of harmonics, The order of the harmonic is denoted as .

[0011] In some implementations, the variable insulation fatigue value is calculated based on the kurtosis and harmonic distortion rate using the following formula:

[0012] in, This is the variable insulation fatigue value. This is the weighting coefficient for kurtosis. As a safe threshold for kurtosis, These are the weighting coefficients for harmonics. The safe threshold for harmonics, For ravine, Harmonic distortion rate; Based on the variable insulation fatigue value and the preset fixed insulation fatigue value, the insulation fatigue coefficient is calculated using the following formula:

[0013]

[0014] in, The insulation fatigue coefficient, To fix the insulation fatigue value, This is the variable insulation fatigue value. It is a fixed cumulative value.

[0015] In some embodiments, the condition of the steam turbine generator is graded and evaluated based on the insulation fatigue coefficient to obtain condition detection results, specifically including: When the insulation fatigue coefficient is less than 60, the condition detection result of the steam turbine generator is in normal operation. When the insulation fatigue coefficient is greater than or equal to 60 and less than 80, the condition detection result of the steam turbine generator is a state of operational concern. When the insulation fatigue coefficient is greater than or equal to 80 and less than 100, the state detection result of the steam turbine generator is an abnormal operating state. When the insulation fatigue coefficient is greater than or equal to 100, the condition detection result of the steam turbine generator is a severely abnormal operating state.

[0016] Secondly, a turbine generator condition monitoring system based on vibration signals includes: The data acquisition module is used to collect vibration information data of the steam turbine generator and perform preprocessing. The insulation fatigue coefficient calculation module is used to perform insulation fatigue calculation based on the pre-processed vibration information data to obtain the insulation fatigue coefficient. The condition detection module is used to classify and evaluate the condition of the steam turbine generator according to the insulation fatigue coefficient, and obtain the condition detection results.

[0017] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the method for detecting the state of a steam turbine generator based on vibration signals.

[0018] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for detecting the state of a steam turbine generator based on vibration signals.

[0019] Fifthly, a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method for detecting the state of a steam turbine generator based on vibration signals.

[0020] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for detecting the condition of a steam turbine generator based on vibration signals. The method involves collecting and preprocessing vibration data from the steam turbine generator; calculating insulation fatigue based on the preprocessed vibration data to obtain an insulation fatigue coefficient; and then classifying and evaluating the condition of the steam turbine generator according to the insulation fatigue coefficient to obtain the condition detection result. This method integrates the complex vibration signal analysis process into a coherent, automatically executable process, thereby achieving a detection mode that requires no complex installation, no downtime, and continuous operation, effectively overcoming the dependence of traditional methods on specific conditions and human experience.

[0021] Furthermore, consistency verification can proactively identify sensor anomalies or data conflicts, ensuring data reliability from the source; per-unit processing normalizes vibration data with different amplitudes and dimensions, establishing a unified benchmark for subsequent calculations, significantly improving the adaptability and stability of the algorithm under different units and operating conditions.

[0022] Furthermore, the insulation fatigue coefficient is composed of a fixed insulation fatigue value and a variable insulation fatigue value calculated based on kurtosis and harmonic distortion rate. This structure forms a dynamic and static combined evaluation model. The fixed insulation fatigue value reflects the cumulative degradation of the foundation over time, while the variable insulation fatigue value responds in real time to the dynamic characteristics of the fault in the vibration signal, thereby achieving a more comprehensive and sensitive integrated evaluation of the generator condition. Attached Figure Description

[0023] Figure 1 A flowchart of a turbine generator state detection method based on vibration signals provided in an embodiment of the present invention; Figure 2 This is a structural diagram of a turbine generator condition detection system based on vibration signals provided in an embodiment of the present invention; Figure 3 A detailed flowchart of the turbine generator state detection method based on vibration signals provided in an embodiment of the present invention; Figure 4 A schematic diagram of the sensor installation position for the turbine generator state detection method based on vibration signals provided in an embodiment of the present invention; Figure 5 A calculation flowchart of the turbine generator state detection method based on vibration signal provided in an embodiment of the present invention; Figure 6A diagram illustrating the coupling relationship between rotor core vibration and frame vibration in the turbine generator condition detection method based on vibration signals provided in this embodiment of the invention. Figure 7 The calculation results of the turbine generator condition detection method based on vibration signal provided in the embodiment of the present invention are shown in the figure, where (a) is the cumulative curve of insulation fatigue and (b) is the change of noise characteristic index. In the diagram, 1 is the sensor; 2 is the steam side; and 3 is the excitation side. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. The content described herein is for explanation rather than limitation of the present invention.

[0025] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, systems, products, or devices.

[0026] like Figure 1 and Figure 3 As shown, the present invention provides a method for detecting the condition of a steam turbine generator based on vibration signals, comprising the following steps: Establish the coupling relationship between rotor core vibration and frame vibration: The electromagnetic force acting on the stator mainly includes tangential force and normal force. The tangential force decreases after vector superposition over most areas. The tangential force is mainly Lorentz force, which acts on the stator bars and windings. The radial force is mainly Maxwell stress, which mainly acts on the core. Therefore, the core bears the greatest vibration energy. Vibration sensors are directly installed on the turbine generator base housing to acquire vibration signals; The data collected by sensor 1 is standardized; Multi-dimensional calculation and analysis are performed on the data after per-unit processing. Kurtosis is calculated using formula (1), and harmonic distortion rate is calculated using formula (2). (1) In the formula: S For data in a certain dimension, n For the total amount of data, K ur It is a ravine.

[0027] (2) In the formula: HThe highest order of harmonics, F b It is the fundamental frequency.

[0028] Define insulation fatigue coefficient K FAT The evaluation of generator operating status mainly consists of fixed insulation fatigue and variable insulation fatigue, and the calculation formula is shown in equation (3): (2) In the formula: K FAT_fix To fix insulation fatigue, K FAT_var For variable insulation fatigue.

[0029] When a sensor malfunction is detected or the sensor is deactivated, only fixed insulation fatigue is recorded; when the sensor is operating normally, both fixed insulation fatigue and variable insulation fatigue are accumulated, where...

[0030] N It is a fixed cumulative value.

[0031]

[0032] In the formula: w kur , w thd These are the weighting coefficients for kurtosis and harmonics, respectively. K ur_thr , THD thr These are the safety thresholds for kurtosis and harmonics, respectively.

[0033] When a generator is under maintenance, insulation fatigue occurs. K FAT Will be cleared.

[0034] The insulation fatigue calculated based on the obtained results K FAT The generator operating status is evaluated in a graded manner: when K FAT When the temperature is less than 60°C, the status detection result of the steam turbine generator is in normal operation, and the generator is judged to be operating normally. When 60≤ K FAT When the value is less than 80, the status monitoring result of the steam turbine generator is in a state of operational concern, reminding users to pay attention to the generator's operating status. When 80≤ K FATWhen the value is less than 100, the status detection result of the steam turbine generator is an abnormal operating state, indicating that the generator is operating abnormally. when K FAT When the value is ≥100, the status detection result of the steam turbine generator is a serious abnormal operating state, indicating that the generator is in a serious abnormal operating state.

[0035] Based on the above methods, such as Figures 4-7 As shown, this embodiment provides a turbine generator condition detection method based on vibration signals, specifically a method for judging inter-turn short-circuit faults in the generator rotor winding, including the following steps: Step S1: Install a vibration sensor on the generator base to collect vibration signal data.

[0036] In step S1, as Figure 6 As shown, it is necessary to first establish the coupling relationship between rotor core vibration and frame vibration: the electromagnetic force acting on the stator mainly includes tangential force and normal force. The tangential force decreases after vector superposition over most areas, and the tangential force is mainly the Lorentz force, acting on the stator bars and windings. The radial force is mainly Maxwell stress, mainly acting on the core. Therefore, the core bears the greatest vibration energy. The expression for the radial force density is finally derived as follows:

[0037] Integration yields radial force waves, which are essentially vibrational force waves.

[0038] Then calculate the natural frequency of the generator base.

[0039] In the formula For the stiffness of the stator core yoke, Concentrated stiffness of the frame For the mass of the stator yoke. For the quality of the stator teeth, M f This represents the average mass of the stator frame.

[0040] When the generator structure is normal, the frequency of the vibration force wave is basically the same as the natural frequency of the generator frame. Therefore, the vibration of the frame can be used to detect the vibration of the generator rotor core.

[0041] Step S2: Standardize the data collected by the vibration sensor for subsequent calculation and analysis.

[0042] In step S2, the acquired generator operating parameters can be sent to the generator rotor winding inter-turn short circuit monitoring system for subsequent processing and storage.

[0043] Step S3: Check the consistency of the sensors to ensure the reliability of the collected data. If the consistency check fails, discard this set of data and only accumulate the fixed insulation fatigue.

[0044] In step S4, based on the information data collected in step 2, the insulation fatigue coefficient is calculated using multi-dimensional data. K FAT Calculate the kurtosis using the following formula:

[0045] In the formula: S For data in a certain dimension, n For the total amount of data, K ur It is a ravine.

[0046] The harmonic distortion rate can be calculated using the following formula:

[0047] In the formula: H The highest order of harmonics, F b It is the fundamental frequency.

[0048] Insulation fatigue coefficient K FAT It mainly consists of fixed insulation fatigue and variable insulation fatigue, and the calculation formula is shown in the following formula:

[0049] In the formula: K FAT_fix To fix insulation fatigue, K FAT_var For variable insulation fatigue.

[0050] Variable insulation fatigue K FAT_var Whether to include the variable insulation fatigue coefficient in the calculation depends on the sensor's operating status and consistency assessment results. K FAT_var It can be calculated using the following formula:

[0051] In the formula: w kur , w thd These are the weighting coefficients for kurtosis and harmonics, respectively. K ur_thr , THD thr These are the safety thresholds for kurtosis and harmonics, respectively.

[0052] Fixed insulation fatigue K FAT_fix It is a fixed value that is automatically accumulated according to a set rule.

[0053] In step S5, the vibration state of the steam turbine generator is determined based on the deviation ratio calculated in step 4. Specifically: like K FAT When the temperature is less than 60°C, the generator is considered to be operating normally. If 60≤ K FAT When the temperature is below 80°C, please pay attention to the generator's operating status. If 80≤ K FAT When the value is less than 100, it indicates that the generator is in an abnormal operating state. like K FAT A value ≥100 indicates a serious abnormality in generator operation; In step S6, based on the judgment result from step S5, it is displayed and human-computer interaction is performed. For example... Figure 7 The figure shows the calculation results of this embodiment.

[0054] like Figure 2 As shown, this embodiment provides a turbine generator condition detection system based on vibration signals, including: The data acquisition module is used to collect and preprocess vibration data from the steam turbine generator; the module includes vibration sensors and a low-frequency filtering circuit. The vibration sensors are installed on both sides of the generator base, such as... Figure 4 As shown, that is Figure 4 Vibration sensors on the 2nd side (engine side) and 3rd side (excitation side) can have digital output capabilities. A low-frequency filter circuit is used to acquire the generator vibration signal for subsequent calculation and analysis.

[0055] The insulation fatigue coefficient calculation module is used to perform insulation fatigue calculations based on preprocessed vibration information data to obtain the insulation fatigue coefficient; according to... Figure 5 The process shown performs data processing and sensor consistency determination, and calculates generator kurtosis, THD, and insulation fatigue based on the determination results and data.

[0056] The condition detection module is used to classify and evaluate the condition of the steam turbine generator based on the insulation fatigue coefficient, and obtain the condition detection results. The calculation results from the insulation fatigue coefficient calculation module are used to determine the generator's operating condition. If the insulation fatigue coefficient is less than 60, the generator is considered to be operating normally; if the insulation fatigue coefficient is greater than or equal to 60 but less than 80, it indicates that the generator's operating condition needs attention, and an alarm signal can be issued to continue monitoring its operating condition; if the insulation fatigue coefficient is greater than or equal to 80 but less than 100, it indicates that the generator has entered an abnormal operating state, and a warning message is issued reminding the generator to perform maintenance; if the insulation fatigue coefficient is greater than or equal to 100, it indicates that the generator has entered a seriously abnormal operating state.

[0057] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0058] This embodiment also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program (in this embodiment, the computer program includes a calculation component and an iterative component, capable of model calculation and model updating). The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a turbine generator state detection method based on vibration signals.

[0059] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the turbine generator state detection method based on vibration signals in the above embodiment.

[0060] This embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements the corresponding steps of the turbine generator state detection method based on vibration signals described in the above embodiment.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the condition of a steam turbine generator based on vibration signals, characterized in that, Includes the following steps: Vibration data of the steam turbine generator are collected and preprocessed. Insulation fatigue calculations are performed based on the preprocessed vibration information data to obtain the insulation fatigue coefficient; The condition of the steam turbine generator is graded and evaluated based on the insulation fatigue coefficient to obtain the condition detection results.

2. The method for detecting the condition of a steam turbine generator based on vibration signals according to claim 1, characterized in that, Vibration data of the steam turbine generator is collected and preprocessed, specifically including: The vibration information data is subjected to consistency verification and per-unit processing to obtain preprocessed vibration information data.

3. The method for detecting the condition of a steam turbine generator based on vibration signals according to claim 1, characterized in that, Insulation fatigue calculations are performed based on the preprocessed vibration data to obtain the insulation fatigue coefficient, specifically including: Calculate kurtosis and harmonic distortion based on the preprocessed vibration information data; Calculate the variable insulation fatigue value based on the kurtosis and harmonic distortion rate; The insulation fatigue coefficient is calculated based on the variable insulation fatigue value and the preset fixed insulation fatigue value.

4. The method for detecting the condition of a steam turbine generator based on vibration signals according to claim 3, characterized in that, Kurtosis and harmonic distortion rates are calculated based on the preprocessed vibration data using the following formula: in, For ravine, For the first Each vibration information data value, The average value of the vibration information data. The total amount of vibration information data, Harmonic distortion rate, The amplitude of the highest order harmonic. The fundamental amplitude, The highest order of harmonics, The order of the harmonic is denoted by .

5. The method for detecting the condition of a steam turbine generator based on vibration signals according to claim 3, characterized in that, The variable insulation fatigue value is calculated based on the kurtosis and harmonic distortion rate using the following formula: in, This is the variable insulation fatigue value. This is the weighting coefficient for kurtosis. As a safe threshold for kurtosis, These are the weighting coefficients for harmonics. The safe threshold for harmonics, For ravine, Harmonic distortion rate; Based on the variable insulation fatigue value and the preset fixed insulation fatigue value, the insulation fatigue coefficient is calculated using the following formula: in, The insulation fatigue coefficient, To fix the insulation fatigue value, This is the variable insulation fatigue value. It is a fixed cumulative value.

6. The method for detecting the condition of a steam turbine generator based on vibration signals according to claim 1, characterized in that, The condition of the steam turbine generator is graded and evaluated based on the insulation fatigue coefficient to obtain the condition monitoring results, which specifically include: When the insulation fatigue coefficient is less than 60, the condition detection result of the steam turbine generator is in normal operation. When the insulation fatigue coefficient is greater than or equal to 60 and less than 80, the condition detection result of the steam turbine generator is a state of operational concern. When the insulation fatigue coefficient is greater than or equal to 80 and less than 100, the state detection result of the steam turbine generator is an abnormal operating state. When the insulation fatigue coefficient is greater than or equal to 100, the condition detection result of the steam turbine generator is a severely abnormal operating state.

7. A turbine generator condition monitoring system based on vibration signals, characterized in that, include: The data acquisition module is used to collect vibration information data of the steam turbine generator and perform preprocessing. The insulation fatigue coefficient calculation module is used to perform insulation fatigue calculation based on the pre-processed vibration information data to obtain the insulation fatigue coefficient. The condition detection module is used to classify and evaluate the condition of the steam turbine generator according to the insulation fatigue coefficient, and obtain the condition detection results.

8. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, it implements the steps of the turbine generator condition detection method based on vibration signals as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the turbine generator condition detection method based on vibration signals as described in any one of claims 1 to 6.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the turbine generator state detection method based on vibration signals as described in any one of claims 1 to 6.

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