Wind power industry equipment state on-line monitoring system and fault analysis method

Abstract

The invention provides a wind power industry equipment state on-line monitoring system and a fault analysis method, and the system comprises a signal collection module, a direct monitoring module, an indirect monitoring module, an impact pulse and vibration analysis module, a multi-trend analysis module, a comprehensive diagnosis module, and an analysis report module. Vibration, impact pulse and phase data of a main shaft, a gear box and the like are collected, a phase information matching fault library is fused to evaluate damage, and the actual change rate is calculated to judge the stable state. Time-frequency domain analysis is carried out to identify the shafting problem, and multi-dimensional analysis is carried out to draw an operation map. Feedback of all the modules is integrated, the fault diagnosis module comprehensively evaluates, an analysis report is finally generated, and accurate monitoring and fault early warning of the wind power equipment are achieved.

Description

Technical Field

[0001] The present invention relates to the field of automated detection technology, and in particular to an online monitoring system for equipment status in the wind power industry and a fault analysis method. Background Art

[0002] Wind farms contain a large number of equipment such as main shafts, gearboxes, planetary packages and generators, which require regular maintenance and troubleshooting to ensure the normal operation of the equipment. However, each device involves multiple components and systems, and traditional operation and maintenance methods often face huge challenges. Existing maintenance methods are mainly based on regular inspections and emergency repairs, which have technical problems such as low efficiency, slow response speed and high maintenance costs. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes an online monitoring system for the equipment status of the wind power industry and a fault analysis method. By collecting real-time operation data of wind power equipment, feature extraction, change rate calculation and time-frequency domain analysis are performed to achieve multi-dimensional fault monitoring, avoid missing minor faults, and improve the timeliness and accuracy of fault monitoring.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides an online monitoring system for equipment status in the wind power industry, comprising: Signal acquisition module, used to collect equipment operating parameters, indirect operating parameters and equipment status indicators; Direct monitoring module, used to obtain equipment operating parameters, compare them with preset values, and perform preliminary fault diagnosis; An indirect monitoring module is used to obtain indirect operating parameters to calculate the actual change rate and perform secondary fault diagnosis based on the actual change rate; Shock pulse and vibration analysis module, used to obtain equipment operating parameters and equipment status indicators, using shock pulse analysis method and high-definition vibration acceleration envelope demodulation method to perform shock pulse and vibration analysis; Multi-trend analysis module, used to draw graphs based on equipment operating parameters and analyze equipment operating trends; Comprehensive diagnostic module, used to comprehensively evaluate equipment operating parameters, indirect operating parameters and equipment status indicators; The analysis report module is used to generate an analysis report by receiving preliminary fault diagnosis results, secondary fault diagnosis results, shock pulse and vibration analysis results, equipment operation trend results and comprehensive evaluation results.

[0005] Preferably, the equipment operating parameters include vibration signals, impact pulse signals, rotation speed and various signs; the indirect operating parameters include wind speed, shaft speed, load and temperature; the equipment status indicators include misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication and gear damage.

[0006] Preferably, the signal acquisition module includes a double T sensor and a rotation speed sensor; The double T sensor is used to collect equipment vibration signals and shock pulse signals; The speed sensor is used to collect equipment speed information and output phase information; when the speed information exceeds the set speed threshold, the direct monitoring module, indirect monitoring module and impact pulse and vibration analysis module are triggered to run.

[0007] Preferably, the preliminary fault diagnosis includes: Obtain equipment operating parameters, perform signal fusion processing, and obtain fusion features; The fused features are matched with the fault rule base to obtain preliminary fault diagnosis results.

[0008] Preferably, the obtaining of indirect operating parameters to calculate the actual change rate and performing secondary fault diagnosis based on the actual change rate specifically includes: obtaining indirect operating parameters to calculate the actual change rate, matching the actual change rate with a change rate rule base, and performing secondary fault diagnosis.

[0009] Preferably, the shock pulse analysis method specifically includes: Filter the collected impact pulse signal, extract the stress wave signal, and amplify the stress wave signal; Extracting characteristic parameters from the amplified signal; the characteristic parameters include signal frequency, amplitude, and duration, and the characteristic parameters are used to reflect the degree of material failure and the lubrication state of the equipment; Based on the extracted characteristic parameters, the equipment status is divided into four status codes, A, B, C, and D, using the preset judgment rules, corresponding to good bearings, poor lubrication, and dry friction with the risk of damage and harm respectively; Collect equipment status codes in the early, middle and late stages of the equipment, and analyze the equipment status change trend by comparing historical data.

[0010] Preferably, the graphs include waveform spectrum graph, fault frequency analysis, axis trajectory, axis position, Bode graph, waterfall graph, single value bar graph and envelope spectrum graph.

[0011] Preferably, the comprehensive evaluation of the equipment operating parameters, indirect operating parameters and equipment status indicators specifically includes: The vibration acceleration, velocity, displacement, crest factor, kurtosis, skewness, and four levels of friction factor, together with the preset symptom matching index, are plotted on a chart. Combining the vibration time domain analysis with the symptom evaluation frequency domain analysis method, the symptom evaluation values ​​and fault display status of imbalance, misalignment, looseness, poor gear meshing, bearing outer ring, inner ring, rolling element, and cage faults are obtained.

[0012] In a second aspect, the present invention provides a method for analyzing equipment failure in the wind power industry, comprising: Collect equipment operating parameters, compare them with preset values, and conduct preliminary fault diagnosis; the equipment operating parameters include vibration value, impact pulse, rotation speed and various signs; Obtain indirect operating parameters to calculate the actual change rate, match the actual change rate with the change rate rule library, and perform secondary fault diagnosis; the indirect operating parameters include wind speed, shaft speed, load and temperature; Obtain equipment status indicators, and use segmented pulse evaluation method and high-definition vibration acceleration envelope demodulation method to perform impact pulse and vibration analysis; the equipment status indicators include misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication and gear damage; Draw graphs based on equipment operating parameters and analyze equipment operating trends; Obtain equipment operating parameters, indirect operating parameters and equipment status indicators for comprehensive evaluation; Receive preliminary fault diagnosis results, secondary fault diagnosis results, shock pulse and vibration analysis results, equipment operation trend results and comprehensive evaluation results to generate analysis reports.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects key data, realizes real-time analysis and evaluation of the health status of the unit equipment, automatically and intelligently diagnoses and warns of related faults in advance, changes post-event processing into pre-event defense, and changes planned or post-event maintenance into state-based predictive inspection and maintenance, thereby avoiding safety hazards and economic losses caused by sudden failures, reducing accident rates and maintenance costs, and reducing the inventory rate of spare parts, thereby providing guarantee for the efficient operation of equipment.

[0014] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0016] Figure 1A schematic diagram of the arrangement of measuring points provided in an embodiment of the present invention; Figure 2 A flow chart of a method for analyzing equipment failure in the wind power industry provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0018] Embodiment 1 This embodiment discloses an online monitoring system for equipment status in the wind power industry, including a signal acquisition module, a direct monitoring module, an indirect monitoring module, an impact pulse and vibration analysis module, a multi-trend analysis module, a comprehensive diagnosis module and an analysis report module.

[0019] Among them, the signal acquisition module is used to collect equipment operating parameters, indirect operating parameters and equipment status indicators; Direct monitoring module, used to obtain equipment operating parameters, compare them with preset values, and perform preliminary fault diagnosis; The indirect monitoring module is used to obtain the indirect operating parameters to calculate the actual change rate, match the actual change rate with the change rate rule library, and perform secondary fault diagnosis; Shock pulse and vibration analysis module, used to obtain equipment status indicators, using segmented pulse evaluation method and high-definition vibration acceleration envelope demodulation method to perform shock pulse and vibration analysis; Multi-trend analysis module, used to draw graphs based on equipment operating parameters and analyze equipment operating trends; Comprehensive diagnostic module, used to comprehensively evaluate equipment operating parameters, indirect operating parameters and equipment status indicators; The analysis report module is used to generate an analysis report by receiving preliminary fault diagnosis results, secondary fault diagnosis results, shock pulse and vibration analysis results, equipment operation trend results and comprehensive evaluation results.

[0020] As a specific implementation method, the equipment operating parameters include vibration values, impact pulses, rotation speeds and various signs; the indirect operating parameters include wind speed, shaft speed, load and temperature; the equipment status indicators include misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication and gear damage.

[0021] As a specific implementation method, the signal acquisition module includes a dual T sensor and a speed sensor. Figure 1 As shown, a double T sensor is installed at the bearing position of the main shaft, each gear shaft of the gear box, every 120° angle in the 360° of the planetary package, and the bearing positions at both ends of the generator to collect equipment vibration signals and impact pulse signals.

[0022] The speed sensor is used as a trigger condition for signal acquisition and phase information is introduced for fault diagnosis. When the acquired speed information exceeds the set speed threshold, the real-time analysis module, the rate of change analysis module and the time-frequency domain analysis module are triggered to run.

[0023] As a specific implementation, the direct monitoring module performs signal fusion on the data, extracts characteristic data and matches it with the fault rule library, draws a fault conclusion and generates a diagnosis report.

[0024] It should be understood that the signal fusion and feature extraction methods can be selected by those skilled in the art according to actual conditions. In this embodiment, a hybrid multi-sensor information fusion method is preferably used to fuse vibration and pulse signals, and a Kalman filter method is combined with a high-definition envelope processing method to extract feature information. The fault rule library contains fault signal features obtained based on experience or historical data.

[0025] As a specific implementation method, the indirect monitoring module acquires the equipment operation data, establishes the alarm threshold and trend of each monitoring point of the equipment, and uses the alarm event method to perform threshold and trend alarms on the equipment status, and supports change rate alarms, matches the actual change rate with the change rate rule library, and performs secondary fault diagnosis. The change rate rule library contains the fault signal change rate characteristics obtained based on experience or historical data. This embodiment has richer and more diverse alarm information, and each parameter can set the most reasonable alarm threshold according to ISO2372 and ISO10816 or customized standards.

[0026] As a specific implementation, the shock pulse and vibration analysis module is used to obtain equipment status indicators and perform shock pulse and vibration analysis using a segmented pulse evaluation method and a high-definition vibration acceleration envelope demodulation method.

[0027] Specifically, shock pulse technology picks up status signals from the early stage of material failure, throughout the early, middle and late stages of equipment failure. Shock pulse technology captures high-frequency, low-energy stress waves generated during material failure. Even in the first stage of material failure, it can be amplified through internal resonance, and an early warning is issued at the first stage of the failure. After four stages, including crack initiation, crack meshing, loose particles, and peeling, the early warning time can reach 3-8 months. Shock pulse technology can accurately evaluate the oil film condition, thereby evaluating the equipment lubrication condition, and obtain four status codes A, B, C, and D: CODE A good bearing; CODE B poor lubrication; CODE C dry friction, with the risk of damage; CODE D injury.

[0028] Specifically, this embodiment utilizes the shock pulse analysis method to capture the high-frequency, low-energy stress waves generated during the material failure process, amplify these signals using internal resonance, and provide early warnings at different stages (early, middle, and late) of the equipment, while evaluating the lubrication status of the equipment.

[0029] Filter the collected original impact pulse signal, extract the stress wave signal, and amplify the stress wave signal; Extracting characteristic parameters from the amplified signal; the characteristic parameters include signal frequency, amplitude, and duration, and the characteristic parameters are used to reflect the degree of material failure and the lubrication state of the equipment; Based on the extracted characteristic parameters, the equipment status is divided into four status codes, A, B, C, and D, using the preset judgment rules, corresponding to good bearings, poor lubrication, and dry friction with the risk of damage and harm respectively; According to the status code of the equipment, a warning or alarm signal is issued to the user. In the early stage of the equipment (such as the beginning stage of cracks), a warning signal can be issued to remind the user to perform maintenance or inspection; in the late stage of the equipment (such as the spalling stage), an alarm signal can be issued to prompt the user to immediately stop the equipment for maintenance.

[0030] The collected signals and evaluation results are stored in a database for subsequent data analysis and trend prediction.

[0031] By comparing historical data, we can analyze the status change trend of the equipment and predict future maintenance needs and potential failure points.

[0032] Vibration analysis is a basic component of equipment status monitoring. The cause of vibration can be analyzed by the size of vibration value and spectrum signal, such as imbalance, misalignment, looseness and resonance and other basic faults. However, the on-site working conditions are complicated and there is strong noise interference (such as environmental noise, equipment noise, etc., which are low-frequency and high-energy signals). How to extract useful fault signals from these strong interference noises? This embodiment uses high-definition vibration acceleration envelope demodulation technology, which can locate basic equipment faults more accurately than traditional vibration technology. Accurate analysis and diagnosis of gear faults is a difficult problem in the industry. This embodiment will collect multiple groups of impact pulses and high-definition vibration data, and obtain clear gear meshing signals after multiple synchronous averaging. The circular coordinate analysis technology is used to evaluate the working condition of the gear, making fault analysis more intuitive and simple. Gear surface problems, gear eccentricity, gear tooth breakage and other faults can be accurately analyzed.

[0033] As a specific implementation method, the multi-trend analysis module can pre-set various equipment failure signs and perform trend analysis on each sign, so as to achieve early monitoring and early prediction of each failure, greatly increase the equipment warning time, and establish a multi-dimensional, multi-graphic display of equipment operation trend chart. The equipment status development trend information reflects the staged health level of the equipment, and can also reflect the speed and severity of the equipment failure deterioration. It can also reflect the impact of process adjustment, environmental change, external interference and other factors on the fatigue wear rate of the equipment. Through the mobile phone APP, users can view its change process anytime and anywhere, and formulate more reasonable equipment use, maintenance and repair plans through trend changes. The mobile phone APP can receive intelligent alarm notifications within the authority according to different permissions, and can alarm the measurement point for failures. Such as: fault alarm, trend alarm, temperature alarm and other contents. The basic status of the equipment is displayed through simple "green, yellow, and red", and the displayed content is normal, warning, fault, offline and other status information.

[0034] As a specific implementation method, the comprehensive diagnosis module establishes an equipment fault diagnosis model and provides two methods: automatic diagnosis and manual diagnosis. Since wind power generation equipment works under various working conditions, it is impossible to adopt a unified evaluation standard. Both the impact pulse technology and the intelligent evaluation can adopt the variable speed and variable load analysis technology, conduct correlation evaluation based on parameters such as wind speed, rotation speed, and power, intelligently adjust the evaluation standard, and make a normalized evaluation value to obtain a reliable analysis conclusion. The measurement parameters that can be used for correlation diagnosis in manual diagnosis are: vibration value, impact pulse, rotation speed, and various signs; the operating parameters that can be used for correlation diagnosis are: wind speed, shaft speed, load, and temperature; normalized equipment status indicators: misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication, and gear damage. The automatic diagnosis function of this embodiment integrates vibration acceleration, velocity, displacement, crest factor, kurtosis, skewness, and 4 levels of friction factor, together with 1 to 45 preset symptom matching indicators, in accordance with ISO10816 standards, on a chart, perfectly combining vibration time domain analysis with efficient symptom evaluation frequency domain analysis methods, turning complex manual analysis into simple automatic diagnosis results. The automatic diagnosis technology combines the time domain analysis results with the symptom fitting in the frequency domain, and can directly obtain 1 to 45 symptom evaluation values ​​and "green, yellow, red" states such as imbalance, misalignment, looseness, poor gear meshing, bearing outer ring, inner ring, rolling element, and cage failure, thereby achieving the purpose of comprehensive evaluation and accurate diagnosis.

[0035] As a specific implementation method, the analysis report module generates an equipment operation status analysis report for the system that contains basic equipment information, alarm information, cumulative operating time and other information, accumulates equipment operation history data, and provides data support for fault diagnosis, inspection and maintenance, and renovation and other work. This embodiment supports "graphic" and "tree structured" dual reading methods. All measurement points, equipment, motors, and water pumps can intelligently display the operating status in "green, yellow, and red". Fully networked software realizes LAN sharing, and the full-time leadership office browses the equipment operation status (intelligent "green, yellow, and red" status indication), and professional engineers implement remote diagnosis and issue diagnostic reports.

[0036] This embodiment collects key data to realize real-time analysis and evaluation of the health status of the unit equipment, automatically and intelligently diagnoses and warns of related faults in advance, changes post-event processing into pre-event defense, and changes planned or post-event maintenance into status-based predictive inspection and maintenance, thereby avoiding safety hazards and economic losses caused by sudden failures, reducing accident rates and maintenance costs, and reducing the inventory rate of spare parts, thereby providing guarantees for the efficient operation of equipment.

[0037] Embodiment 2 like Figure 2 As shown, this embodiment discloses a method for analyzing equipment failure in the wind power industry, based on an online monitoring system for the status of equipment in the wind power industry provided in Embodiment 1, comprising the following steps: S1: Collect equipment operating parameters, compare them with preset values, and perform preliminary fault diagnosis; the equipment operating parameters include vibration value, impact pulse, rotation speed and various signs; S2: Obtain indirect operating parameters to calculate the actual change rate, match the actual change rate with the change rate rule library, and perform secondary fault diagnosis; the indirect operating parameters include wind speed, shaft speed, load and temperature; S3: Obtain equipment operating parameters and equipment status indicators, and use impact pulse analysis method and high-definition vibration acceleration envelope demodulation method to perform impact pulse and vibration analysis; the equipment status indicators include misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication and gear damage; S4: Draw a graph based on the equipment operating parameters and analyze the equipment operating trend; S5: Obtain equipment operating parameters, indirect operating parameters and equipment status indicators for comprehensive evaluation; S6: Receive preliminary fault diagnosis results, secondary fault diagnosis results, shock pulse and vibration analysis results, equipment operation trend results and comprehensive evaluation results to generate an analysis report.

[0038] The steps involved in the above embodiment 2 correspond to those in embodiment 1. For the specific implementation method, please refer to the relevant description part of embodiment 1.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An online monitoring system for equipment status in the wind power industry, It is characterized in that include: Signal acquisition module, used to collect equipment operating parameters, indirect operating parameters and equipment status indicators; Direct monitoring module, used to obtain equipment operating parameters, compare them with preset values, and perform preliminary fault diagnosis; An indirect monitoring module is used to obtain indirect operating parameters to calculate the actual change rate and perform secondary fault diagnosis based on the actual change rate; Shock pulse and vibration analysis module, used to obtain equipment operating parameters and equipment status indicators, using shock pulse analysis method and high-definition vibration acceleration envelope demodulation method to perform shock pulse and vibration analysis; Multi-trend analysis module, used to draw graphs based on equipment operating parameters and analyze equipment operating trends; Comprehensive diagnostic module, used to comprehensively evaluate equipment operating parameters, indirect operating parameters and equipment status indicators; The analysis report module is used to generate an analysis report by receiving preliminary fault diagnosis results, secondary fault diagnosis results, shock pulse and vibration analysis results, equipment operation trend results and comprehensive evaluation results.

2. An online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The equipment operating parameters include vibration signals, impact pulse signals, rotation speed and various signs; the indirect operating parameters include wind speed, shaft speed, load and temperature; the equipment status indicators include misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication and gear damage.

3. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The signal acquisition module includes a double T sensor and a rotation speed sensor; The double T sensor is used to collect equipment vibration signals and shock pulse signals; The speed sensor is used to collect equipment speed information and output phase information; when the speed information exceeds the set speed threshold, the direct monitoring module, indirect monitoring module and impact pulse and vibration analysis module are triggered to run.

4. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The preliminary fault diagnosis includes: Obtain equipment operating parameters, perform signal fusion processing, and obtain fusion features; The fused features are matched with the fault rule base to obtain preliminary fault diagnosis results.

5. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The obtaining of indirect operating parameters to calculate the actual change rate, and performing secondary fault diagnosis based on the actual change rate, specifically includes: obtaining indirect operating parameters to calculate the actual change rate, matching the actual change rate with a change rate rule base, and performing secondary fault diagnosis.

6. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The shock pulse analysis method specifically includes: Filter the collected impact pulse signal, extract the stress wave signal, and amplify the stress wave signal; Extracting characteristic parameters from the amplified signal; the characteristic parameters include signal frequency, amplitude, and duration, and the characteristic parameters are used to reflect the degree of material failure and the lubrication state of the equipment; Based on the extracted characteristic parameters, the equipment status is divided into four status codes, A, B, C, and D, using the preset judgment rules, corresponding to good bearings, poor lubrication, and dry friction with the risk of damage and harm respectively; Collect equipment status codes in the early, middle and late stages of the equipment, and analyze the equipment status change trend by comparing historical data.

7. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The high-definition vibration acceleration envelope demodulation method comprises: Adopting high-definition vibration acceleration envelope demodulation algorithm, multiple groups of impact pulses and high-definition vibration data collected are synchronously averaged multiple times to obtain clear gear meshing signals; The circular coordinate analysis technology is used to evaluate the working condition of the gear and obtain the gear surface problems, gear eccentricity, and gear tooth breakage fault analysis results.

8. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The graphs include waveform spectrum, fault frequency analysis, axis trajectory, axis position, Bode diagram, waterfall diagram, single value bar graph and envelope spectrum.

9. The online monitoring system for equipment status in the wind power industry as claimed in claim 1, It is characterized in that The comprehensive evaluation of equipment operating parameters, indirect operating parameters and equipment status indicators specifically includes: The vibration acceleration, velocity, displacement, crest factor, kurtosis, skewness, and four levels of friction factor, together with the preset symptom matching index, are plotted on a chart. Combining the vibration time domain analysis with the symptom evaluation frequency domain analysis method, the symptom evaluation values ​​and fault display status of imbalance, misalignment, looseness, poor gear meshing, bearing outer ring, inner ring, rolling element, and cage faults are obtained.

10. A method for analyzing equipment failure in the wind power industry. It is characterized in that include: Collect equipment operating parameters, compare them with preset values, and conduct preliminary fault diagnosis; the equipment operating parameters include vibration value, impact pulse, rotation speed and various signs; Obtain indirect operating parameters to calculate the actual change rate, match the actual change rate with the change rate rule library, and perform secondary fault diagnosis; The indirect operating parameters include wind speed, shaft speed, load and temperature; Obtain equipment operating parameters and equipment status indicators, and use shock pulse analysis and high-definition vibration acceleration envelope demodulation methods to perform shock pulse and vibration analysis; the equipment status indicators include misalignment, imbalance, looseness, wear, bearing damage, bearing lubrication, and gear damage; Draw graphs based on equipment operating parameters and analyze equipment operating trends; Obtain equipment operating parameters, indirect operating parameters and equipment status indicators for comprehensive evaluation; Receive preliminary fault diagnosis results, secondary fault diagnosis results, shock pulse and vibration analysis results, equipment operation trend results and comprehensive evaluation results to generate analysis reports.

Images