A non-contact vibration detection method for mine ventilators

Abstract

The present invention provides a non-contact vibration detection method for mine ventilators, belonging to the technical field of coal mine safety monitoring and control. This method uses non-contact optical sensing technology to collect the original vibration signals, adaptively extracts high-precision vibration data based on the Kalman filtering algorithm, realizes the unconstrained, non-contact, and high-precision acquisition of vibration information in the emergency mode. It adopts an improved Fourier spectrum analysis method and a trend-based fault diagnosis strategy to reliably and effectively solve the problem of analyzing the vibration faults of ventilators. Based on wireless network technology, it studies the emergency communication mode and method to solve the problem of real-time and reliable transmission of early warning information. It uses new technologies such as big data and artificial intelligence to further explore the data value in the cloud, converts the measurement data into effective information, and provides support for data fusion and early warning linkage. The present invention has four technical characteristics: flexible, precise, intelligent, and interconnected, providing a perfect technical solution for vibration monitoring and fault early warning in the emergency mode.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine safety monitoring and control, and specifically to a non-contact vibration detection method for mine ventilators. Background Technique

[0002] Mine ventilators are important production equipment in coal mines, mainly composed of fans, main motors, couplings, and electric control systems, etc. During long-term operation, various faults will inevitably occur. Therefore, it is necessary to monitor the operating status of mine ventilators in real time to ensure their safe and stable operation, and thus guarantee the smooth progress of coal mine production.

[0003] Vibration is an important parameter for evaluating the operating status of ventilators, and vibration signal analysis is the core content of ventilator status monitoring. In order to ensure the safe and reliable operation of mine ventilators, the fault analysis technology based on vibration detection has always been a research hotspot in the field of work safety.

[0004] In recent years, domestic and foreign researchers have carried out a large amount of research work on vibration detection and analysis and achieved certain research results. These results have played a positive role in improving the safety and stability of ventilator operation; however, with the progress of the economic society, the demand for emergency management systems and capacity building in all walks of life is increasing, and higher requirements are also put forward for the vibration signal detection technology in the electromechanical field; the current vibration detection methods have no obvious emergency management attributes and are difficult to meet the development needs of intelligent emergency. Therefore, under the new situation, it is of great significance and value to carry out research on non-contact vibration signal detection methods for emergency.

[0005] In summary, in order to promote the informatization of emergency management and further improve the equipment fault analysis ability, it is necessary to study the laws and methods of vibration signal detection in the emergency mode. Aiming at the problems existing in traditional vibration detection technologies in aspects such as information collection, data analysis, and early warning linkage, the present invention designs a non-contact vibration detection method for mine ventilators to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a non-contact vibration detection method for mine ventilators to solve the problems raised in the above background technique.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A non-contact vibration detection method for mine ventilators, including the following steps:

[0008] S1: Construct an intelligent vibration acquisition and analysis system based on the emergency mode. The system consists of an intelligent vibration detector, an edge computing platform, a display and warning unit, and a cloud data server;

[0009] S2: The intelligent vibration detector is designed based on the principle of laser triangulation, and is distributedly installed at the planned measuring points. It uses technologies such as optical sensing, wireless communication, and mobile power supply to achieve unconstrained and non-contact acquisition of vibration signals and wireless interconnection.

[0010] S3: Through depth-of-field control, vibration information is collected within a small range near the reference position of the vibration detector.

[0011] S4: Based on the core algorithm model, a Kalman filtering program is designed, and the Kalman filtering algorithm is used to process the original vibration measurement data in real time.

[0012] S5: The intelligent vibration detectors distributedly installed on-site collect displacement, temperature, and vibration data of the planned measuring points, and upload them to the edge computing platform through the CC1101 wireless communication network. On the edge side, based on various types of collected measurement data, faults are identified nearby and rapid early warning and protection are carried out. The identified fault types include geometric faults, temperature rise faults, and vibration faults. In the cloud, various types of collected measurement data are automatically uploaded to the cloud server through WLAN, and new technologies such as big data and artificial intelligence are used to further explore the data value and convert the measurement data into effective information.

[0013] Preferably, the intelligent vibration detector consists of two parts: an optical path system and an electrical circuit system. The optical path system is responsible for non-contact sensing of vibration information based on the principle of laser triangulation, and the electrical circuit system takes the CC430F6137 embedded chip as the core and is responsible for the acquisition, display, and wireless interconnection of the original vibration information.

[0014] Preferably, the intelligent vibration detector has functions of automatically measuring various parameters such as displacement, temperature, and vibration, and can provide reliable original measurement data for the diagnosis of geometric faults, temperature rise faults, and vibration faults. The intelligent vibration detector is designed with two working modes: single machine and networking, realizing the fusion and sharing of information and meeting the requirements of emergency early warning linkage.

[0015] Preferably, the design parameters of the intelligent vibration detector are: reference distance 30mm, amplitude measurement range ±4mm, sampling period 500μs. In order to reduce the non-linear error, through depth-of-field control, vibration information is collected within a small range near the reference position of the vibration detector to improve the original measurement accuracy of the vibration signal.

[0016] Preferably, the edge computing platform receives the vibration data uploaded by each vibration detection node through the wireless sensor network, and based on the vibration characteristics of the rotating machinery, an improved Fourier spectrum analysis method is used to solve the contradiction between the frequency subdivision and real-time performance of the FFT algorithm.

[0017] Preferably, the edge computing platform is designed with the intelligent data acquisition and analysis module launched by Advantech.

[0018] Preferably, based on the warning threshold at typical rotational speeds measured experimentally and the quadratic polynomial least squares fitting algorithm, a threshold trend model is solved, and a trend-based fault diagnosis strategy is adopted to reliably and effectively solve the problem of identifying the vibration faults of the ventilator.

[0019] Preferably, various types of measurement data collected are automatically uploaded to the cloud server through the WLAN wireless local area network, and technologies such as big data and artificial intelligence are used to deeply explore the data value, convert the measurement data into effective information, and provide support for data fusion and early warning linkage.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention breaks through the limitations of traditional vibration detection methods in aspects such as information perception, data analysis, and early warning linkage, and proposes a new intelligent vibration detection and analysis method based on the emergency mode. This method uses non-contact optical sensing technology to collect the original vibration signal, adaptively extracts high-precision vibration data based on the Kalman filtering algorithm, realizes the unconstrained and high-precision acquisition of vibration information in the emergency mode, adopts an improved Fourier spectrum analysis method and a trend-based fault diagnosis strategy to reliably and effectively solve the problem of analyzing the vibration faults of the ventilator, and uses new technologies such as big data and artificial intelligence to further explore the data value in the cloud, convert the measurement data into effective information, and provide support for data fusion and early warning linkage; The present invention has four technical characteristics: flexible, precise, intelligent, and interconnected, providing a perfect technical solution for vibration monitoring and fault early warning in the emergency mode.

[0021] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0023] Figure 1 is a schematic diagram of a vibration detection and analysis system based on the emergency mode;

[0024] Figure 2 is a principle block diagram of an intelligent vibration detector;

[0025] Figure 3 is a structural diagram of the circuit system of an intelligent vibration detector;

[0026] Figure 4 is an analysis diagram of the Kalman filtering effect;

[0027] Figure 5 It is a trend chart of a variable threshold warning mode based on trends;

[0028] Figure 6 It is a flow chart of the least squares fitting algorithm for threshold trends;

[0029] Figure 7 It is a flow chart of fault warning in the emergency mode. Specific implementation manners

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0031] Please refer to Figure 1-7 , the present invention provides a technical solution: a non-contact vibration detection method for a mine ventilation fan, which uses an intelligent detection system based on the emergency mode as shown in Figure 1 to collect and analyze vibration information. The system consists of four parts: an intelligent vibration detector, an edge computing platform, a display and warning unit, and a cloud data server. Among them, the intelligent vibration detectors are distributedly installed at the planned measurement points, and technologies such as optical sensing, wireless communication, and mobile power are used to achieve unconstrained, non-contact collection and wireless interconnection of vibration signals; the edge computing platform integrates high-performance processors and vibration time-frequency domain algorithms internally, receives the monitoring data uploaded by each intelligent detection node through a wireless sensor network, and uses intelligent vibration analysis software to achieve functions such as edge-side vibration time-frequency domain analysis, information display, and local warning; information such as vibration data, machine conditions, and fault parameters is uploaded to the cloud data server through a wireless local area network, and technologies such as big data, artificial intelligence, and cloud computing are used to deeply explore the data value and achieve equipment predictive maintenance and linkage warning.

[0032] Among them, as shown in Figure 2 , the intelligent vibration detector is designed based on the principle of laser triangulation. Its internal mainly consists of two parts: an optical path system and a circuit system. The optical path system is responsible for non-contact sensing of vibration information based on the principle of laser triangulation, mainly including key components such as a semiconductor laser, a converging lens, an imaging lens, and a CMOS photosensitive device. In order to achieve precise focusing with a large depth of field, the spatial position relationship of each optical component must satisfy the Scheimpflug's law.

[0033] Let the angles between the incident laser, the CMOS photosensitive device and the principal optical axis of the imaging lens be θ and φ respectively. Let the distances from the object point O and the image point O' at the reference position to the imaging lens be L and L', and the position change amounts of the object point and the image point be X and X' respectively. The measurement model of the vibration detector is solved by the geometric analysis method, as shown in formula (1).

[0034]

[0035] In formula (1), if the measured surface moves down from the reference position, X' is taken as negative, otherwise X' is taken as positive. According to formula (1), the vibration information of the measured object can be accurately solved. The design parameters of the intelligent vibration detector are: reference distance 30 mm, amplitude measurement range ±4 mm, sampling period 500 μs.

[0036] Through the derivation of trigonometric relations, the error model of the vibration detector is established.

[0037] E L = K1X + K2X 2 (2)

[0038] In formula (2), the coefficients K1 and K2 are determined by the structural parameters of the detector and the tilt angle of the object surface. This model is non-linear, and the error increases with the increase of the depth of field. By controlling the depth of field, the vibration information is collected in a small range near the reference position of the vibration detector to reduce the non-linear error and improve the measurement accuracy.

[0039] Among them, the circuit system structure of the intelligent vibration detector is as Figure 3 shown. Among them, the high-speed processor is the core of the circuit system, designed with the CC430F6137 embedded chip of Texas Instruments. This chip has a built-in high-precision AD and CC1101 wireless radio frequency module, with characteristics such as low power consumption and mature technology, and is responsible for the acquisition, display and wireless interconnection of the original vibration information.

[0040] Among them, the intelligent vibration detector adopts CC1101 wireless communication technology and mobile power technology to improve flexibility and emergency attributes. By optimizing the internal clock system, Vcore voltage level, chip working mode, etc., the power consumption of the circuit system is effectively reduced. It is powered by a lithium-ion battery and has functions such as detection and management of battery parameters such as voltage, power and temperature to ensure the continuous and stable operation of the vibration detector in an emergency state.

[0041] Among them, the intelligent vibration detector has functions of automatically measuring various parameters such as displacement, temperature and vibration, and can provide reliable original measurement data for the diagnosis of shape and position faults, temperature rise faults and vibration faults. The intelligent vibration detector is designed with two working modes: single machine and networking, realizing the fusion and sharing of information and meeting the requirements of emergency warning linkage.

[0042] Among them, based on the core algorithm models (3) to (9), a Kalman filtering program is designed to use the Kalman filtering algorithm to process the original vibration measurement data in real time, effectively filtering out the noise interference in the signal and improving the accuracy of vibration detection.

[0043] x(k) = Ax(k - 1) + Bu(k - 1) + w(k - 1) (3)

[0044] y(k) = Hx(k) + v(k) (4)

[0045] x p (k) = Ax c (k - 1) + Bu(k - 1) (5)

[0046] s p (k) = As c (k - 1)A T + Q (6)

[0047]

[0048] x c (k) = x p (k) + K k [y(k) - Hx p (k)] (8)

[0049] s c (k) = (1 - K k H)s p (k) (9)

[0050] Parameter description: x[[ID=4�]] p (k) is the prior estimate value of the parameter at time k, s p (k) is the prior estimate value of the variance at time k, x c (k - 1) and x c (k) respectively represent the posterior estimate values (also called the optimal estimates) of the parameter at time k - 1 and time k, s c (k - 1) and s c (k) respectively represent the posterior estimate values of the variance (also called the combined variance) at time k - 1 and time k, K k is the filtering gain, y(k) is the measured value, Q is the system process variance, R is the measurement noise variance, H is the transformation matrix from the state variable to the observable quantity, A is the state transition matrix, and B is the matrix that converts the control quantity into the state.

[0051] Among them, the edge computing platform is designed with the intelligent data acquisition and analysis module launched by Advantech. The module integrates a high-performance processor inside, providing edge-based data acquisition and analysis capabilities. It has a compact structure, simple operation, and an industrial-grade wide-range power supply to ensure stable and reliable operation in case of emergencies.

[0052] The edge computing platform receives the vibration data uploaded by each vibration detection node through the wireless sensor network, adopts an improved Fourier spectrum analysis method to solve the contradiction between frequency subdivision and algorithm real-time performance, and defines the window length.

[0053] N w = T t * F s (10)

[0054] In formula (10), Fs is the signal sampling rate, Tt is the FFT transformation time. Reasonably selecting the window length Nw can shorten the FFT transformation time, thus ensuring the real-time performance of the algorithm. According to the vibration characteristics of rotating machinery, the sampling data sequence can be periodically filled and supplemented by window sampling data. By optimizing the selection of the data sequence, the contradiction between frequency subdivision and real-time performance of the FFT algorithm is solved.

[0055] Among them, the edge computing platform adopts a trend-based fault diagnosis strategy to reliably and effectively solve the problem of identifying the vibration faults of the ventilator. For rotating machinery, the vibration intensity increases with the increase of the rotational speed. Under different rotational speed conditions, the alarm threshold should also be adjusted accordingly. As Figure 5 shown, A T (V1), A T (V2), …, A T (V n ) are the fault warning thresholds at different rotational speeds solved based on the trend. Comparing the vibration intensity monitored in real time with the alarm threshold A T (Vi) at the corresponding rotational speed, when it is greater than the variable threshold A T (Vi), an alarm is triggered, where i = 1, 2, …, n.

[0056] A quadratic polynomial is used to construct the threshold trend model.

[0057] A T (V) = a2V 2 + a1V + a0 (11)

[0058] In formula (11), V is the independent variable, representing the equipment operating speed, A T (V) is the dependent variable, representing the variable threshold based on the trend, and a0, a1, a2 are the model coefficients. Based on the warning thresholds at typical rotational speeds measured through experiments and the least squares fitting algorithm, the threshold trend model is solved. The algorithm flow is as Figure 6 shown.

[0059] Among them, the early warning strategy shown as Figure 7 is adopted to realize the analysis and early warning of the faults of the mine ventilator. The intelligent vibration detectors distributed on-site collect the displacement, temperature and vibration data of the planned measuring points, and upload them to the edge computing platform through the CC1101 wireless communication network. On the edge side, based on various types of measurement data collected, the faults are identified nearby and quickly warned and protected. The identified fault types include geometric position faults, temperature rise faults and vibration faults. In the cloud, various types of measurement data collected are automatically uploaded to the cloud server through WLAN, and new technologies such as big data and artificial intelligence are used to further explore the data value, convert the measurement data into effective information, such as trends, service lives, etc., to provide support for data fusion and early warning linkage.

[0060] The traditional vibration detection mode uses an acceleration sensor to sense vibration information. The sensor is relatively large in size and is installed in a magnetic adsorption contact manner, which limits the adaptability of the measurement and is difficult to solve the problem of collecting vibration information inside complex machinery. Using the acceleration secondary signal as the evaluation parameter of the vibration state is not conducive to improving the measurement accuracy. In addition, the acceleration sensor is only suitable for dynamic vibration analysis, cannot sense static absolute quantity information, and cannot comprehensively master the vibration characteristics of the measured object.

[0061] A specific application of this embodiment is: First, refer to Figure 1 , construct an intelligent vibration acquisition and analysis system based on the emergency mode. The system consists of four parts: an intelligent vibration detector, an edge computing platform, a display and warning unit, and a cloud data server. Further, refer to Figure 2 and Figure 3 , the intelligent vibration detector is designed based on the laser triangulation principle, distributedly installed at the planned measuring points, and uses technologies such as optical sensing, wireless communication, and mobile power supply to realize the unconstrained, non-contact acquisition and wireless interconnection of vibration signals. Further, in order to reduce the non-linear error, through depth of field control, the vibration information is collected in a small range near the reference position of the vibration detector to improve the original measurement accuracy of the vibration signal. Further, based on the core algorithm models (3) to (9), a Kalman filter program is designed, and the Kalman filter algorithm is used to process the original vibration measurement data in real time to effectively filter out the noise interference in the signal. Further, refer to Figure 7, the intelligent vibration detectors distributed and installed on-site collect the displacement, temperature and vibration data of the planned measurement points, and upload them to the edge computing platform through the CC1101 wireless communication network. On the edge side, based on various types of collected measurement data, faults are identified nearby and early warnings are given for protection. The identified fault types include geometric faults, temperature rise faults and vibration faults. In the cloud, various types of collected measurement data are automatically uploaded to the cloud server through WLAN, and new technologies such as big data and artificial intelligence are used to further explore the data value, convert the measurement data into effective information, such as trends, lifetimes, etc., to provide support for data fusion and early warning linkage. The present invention has four technical characteristics: flexibility, precision, intelligence and interconnection, providing a perfect technical solution for vibration monitoring and fault early warning in the emergency mode.

[0062] In the description of this specification, the descriptions with reference to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0063] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A non-contact vibration detection method for mine ventilators, characterized in that: It includes the following steps: S1: Construct an intelligent vibration acquisition and analysis system based on the emergency mode. The system consists of an intelligent vibration detector, an edge computing platform, a display and warning unit, and a cloud data server; S2: The intelligent vibration detector is designed based on the laser triangulation principle and is distributedly installed at the planned measuring points. It uses optical sensing, wireless communication, and mobile power supply to achieve unconstrained, non-contact acquisition and wireless interconnection of vibration signals; S3: Through depth-of-field control, select to collect vibration information within a small range near the reference position of the intelligent vibration detector; S4: Based on the core algorithm model, design a Kalman filter program and use the Kalman filter algorithm to process the original vibration measurement data in real time; S5: The intelligent vibration detectors distributedly installed on-site collect displacement, temperature, and vibration data of the planned measuring points and upload them to the edge computing platform through the CC1101 wireless communication network. On the edge side, based on various types of measurement data collected, identify faults nearby and quickly give early warnings and protection. The identified fault types include geometric faults, temperature rise faults, and vibration faults. In the cloud, various types of measurement data collected are automatically uploaded to the cloud data server through WLAN, and big data and artificial intelligence are used to further explore the data value and convert the measurement data into effective information; The intelligent vibration detector consists of two parts: an optical path system and a circuit system. The optical path system is responsible for non-contact sensing of vibration information based on the laser triangulation principle. The circuit system takes the CC430F6137 embedded chip as the core and is responsible for the acquisition, display, and wireless interconnection of the original vibration information; The intelligent vibration detector has the function of automatically measuring displacement, temperature, and vibration parameters, providing reliable original measurement data for the diagnosis of geometric faults, temperature rise faults, and vibration faults. The intelligent vibration detector is designed with two working modes: single machine and networking, realizing the fusion and sharing of information and meeting the requirements of emergency warning linkage; The edge computing platform receives the vibration data uploaded by each vibration detection node through the wireless sensor network and uses an improved Fourier spectrum analysis method to solve the contradiction between frequency subdivision and real-time performance of the FFT algorithm according to the vibration characteristics of the rotating machinery; Based on the warning threshold measured in the experiment at a typical rotational speed and the quadratic polynomial least squares fitting algorithm, solve the threshold trend model, and adopt a trend-based fault diagnosis strategy to reliably and effectively solve the problem of identifying the vibration faults of the ventilator.

2. The non-contact vibration detection method for a mine ventilator according to claim 1, characterized in that: The design parameters of the intelligent vibration detector are as follows: the reference distance is 30 mm, the amplitude measurement range is ±4 mm, and the sampling period is 500 μs. In order to reduce the non-linear error, through depth-of-field control, select to collect vibration information within a small range near the reference position of the intelligent vibration detector to improve the original measurement accuracy of the vibration signal.

3. A non-contact vibration detection method for a mine ventilator according to claim 1, characterized in that: Various types of measurement data collected are automatically uploaded to the cloud data server through the WLAN wireless local area network, and big data and artificial intelligence are used to deeply explore the data value and convert the measurement data into effective information, providing support for data fusion and warning linkage.

Images