CN111733499A - Intelligent spinning roving fault detection system - Google Patents
Intelligent spinning roving fault detection system Download PDFInfo
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- CN111733499A CN111733499A CN202010500508.3A CN202010500508A CN111733499A CN 111733499 A CN111733499 A CN 111733499A CN 202010500508 A CN202010500508 A CN 202010500508A CN 111733499 A CN111733499 A CN 111733499A
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Abstract
The invention discloses an intelligent spinning roving fault detection system which comprises a data acquisition module, a control module, a fault detection module, a state display module and an information transmission module, wherein the control module is respectively connected with the data acquisition module, the fault detection module and the state display module through the information transmission module; by the mode, the vibration state of the roller can be monitored in real time, corresponding faults are found in time, fault detection and classification are automatically carried out, timely maintenance is facilitated, and influences caused by the faults are reduced; and the vibration state of the roller is reasonably predicted, potential faults are predicted, advanced treatment is facilitated, the fault occurrence rate is reduced, and the safety and reliability of the roving process are improved.
Description
Technical Field
The invention relates to the technical field of spinning fault detection, in particular to an intelligent spinning roving fault detection system.
Background
With the acceleration of the industrialization process, the spinning industry is rapidly developed, and a mature spinning process and automatic equipment corresponding to each process are formed. At present, the spinning process mainly includes the processes of opening and picking, carding, drawing, roving, spinning, post-processing and the like, wherein the roving is used as the last preparation process before spinning, and if a fault occurs, the smooth proceeding of the roving process is influenced, the product quality of the roving is also influenced, and further, the proceeding of the subsequent spinning process and the final yarn quality are influenced. Therefore, in order to ensure smooth proceeding of roving and subsequent processes and improve product quality, the method has important significance for timely detecting and processing faults in the roving process.
Currently, the roving process is generally performed in a roving frame, and the failure thereof mainly results from mechanical failure of the roving frame. The main task of the roving frame is to draw and thin the drawn sliver provided by the drawing process according to a certain drafting multiple, improve the parallel straightness of the fiber, and wind and form the drawn sliver with proper twist, so as to facilitate the use of the spinning process; the roving frame mainly comprises a drafting mechanism, a twisting mechanism, a winding mechanism and a forming mechanism, which are respectively controlled by a roller drafting motor, a flyer rotating motor, a bobbin winding motor and a keel lifting motor, and the normal operation of each motor is the basis of the normal operation of the roving frame, so that the roving frame controlled by a programmable logic controller at present is usually provided with an encoder for monitoring the rotating speed of each motor so as to identify the corresponding motor fault. However, the method can only detect the motor fault, but cannot find the faults of other mechanical parts in the roving frame, and has low fault detection rate and great hidden danger.
Among various mechanical parts of the roving frame, the roller is used as a main part of a drafting mechanism, which plays an important role in the drafting process of drawn slivers, and mechanical waves generated by roller vibration in the drafting process have a large influence on the uniformity of yarns, so that the current roller faults are mainly detected indirectly by carrying out uniformity test on finished yarns, and because the quality inspection on the finished yarns is usually carried out after the production is finished, the real-time performance is lacked, the prediction cannot be carried out in advance, the faults are usually manually analyzed, the speed is slow, a large number of defective products are generated when the faults are detected, and the whole quality of the products is greatly influenced. In addition, with the wide application of the sensor in textile machinery, a method for detecting faults of the roller by using the vibration sensor is available at present, however, the vibration state of the roller cannot be comprehensively reflected only by vibration acquisition of the roller by the sensor, the detection result is not accurate enough, and partial faults are difficult to detect, so that the method has certain limitations. Therefore, it is necessary to research a fault detection system for a roller in a roving frame, which can timely and effectively detect faults to increase the detectable rate of faults in the roving process, reduce the influence caused by the faults, predict possible faults and improve the safety and reliability of the roving process.
Disclosure of Invention
The invention aims to solve the problems and provides an intelligent spinning roving fault detection system, which can timely find corresponding faults and automatically detect and classify the faults by monitoring the vibration state of a roller in real time, is convenient for relevant personnel to maintain in time and reduces the influence caused by the faults; and the vibration state of the normal roller is reasonably predicted, so that potential faults are predicted, advanced treatment is facilitated, the fault occurrence rate is reduced, and the safety and reliability of the roving process are improved.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent spinning roving fault detection system comprises a data acquisition module, a control module, a fault detection module, a state display module and an information transmission module, wherein the control module is respectively connected with the data acquisition module, the fault detection module and the state display module through the information transmission module; the data acquisition module comprises a vibration signal acquisition unit and an image signal acquisition unit which are respectively used for acquiring the vibration signal and the image signal of the roller in the roving frame in real time; the fault detection module comprises a feature extraction unit, a fault classification unit and a fault prediction unit, wherein the input end of the feature extraction unit is connected with the control module, the output end of the feature extraction unit is connected with the input end of the fault classification unit, the output end of the fault classification unit is respectively connected with the control module and the input end of the fault prediction unit, and the output end of the fault prediction unit is connected with the control module.
Further, the vibration signal acquisition unit includes a plurality of current vortex sensor, current vortex sensor sets up in the front side of roller to make the horizontal diameter of roller pass the center of current vortex sensor probe is used for gathering the vibration signal of roller, and transmits to control module.
Further, the image signal acquisition unit includes a plurality of image acquisition devices that constitute by industry camera and LED lamp, image acquisition device sets up in the top of roller to make the vertical diameter of roller pass the center of industry camera lens among the image acquisition device for the image when gathering the roller vibration, and transmit to control module.
Furthermore, the control module comprises a signal processing unit, an image processing unit, a main control unit and a storage unit, wherein the signal processing unit comprises a signal amplifier and an A/D converter, the input end of the signal amplifier is connected with the vibration signal acquisition unit, the output end of the signal amplifier is connected with the input end of the A/D converter, and the output end of the A/D converter is connected with the main control unit; the image processing unit is used for carrying out gray level processing and binaryzation on an input image, reducing the image memory and improving the subsequent processing speed, the input end of the image processing unit is connected with the image signal acquisition unit, and the output end of the image processing unit is connected with the main control unit; the main control unit is also connected with the storage unit, the fault detection module and the state display module respectively.
Furthermore, the feature extraction unit comprises a wavelet analysis layer, an image analysis layer and a feature vector extraction layer, wherein the wavelet analysis layer is used for performing wavelet transformation and orthogonal wavelet decomposition on the received vibration signals and inputting the vibration signals of each frequency band obtained after decomposition into the feature vector extraction layer; the image analysis layer is used for carrying out edge detection and amplitude calculation on the received image signal and inputting the result into the feature vector extraction layer; the characteristic vector extraction layer is used for arranging the energy values of the received vibration signals according to a scale sequence, forming a characteristic vector together with the amplitude value input by the image analysis layer and outputting the characteristic vector to the fault classification unit.
Further, the fault classification unit comprises a memory, a training layer and a neural network classification layer, the memory is used for storing historical fault data of the roller, the training layer conducts neural network training based on the historical fault data in the memory, the neural network classification layer conducts fault classification on input feature vectors based on the trained neural network, and fault classification results are output to the fault prediction unit and the control module.
Further, the fault classification result comprises normal, roller bending fault, roller eccentricity fault, roller gear defect and roller gear rotation unbalance.
Further, the fault prediction unit comprises a data extraction layer, a curve fitting layer and a result prediction layer, wherein the data extraction layer is used for receiving the fault classification result, periodically extracting the data with the normal fault classification result according to a fixed frequency, and inputting the extracted data into the curve fitting layer; the curve fitting layer is used for performing curve fitting on the data to obtain a prediction curve and outputting the prediction curve to the result prediction layer; and the result prediction layer is used for matching the prediction curve with a set threshold value and outputting the prediction result to the control module.
Furthermore, the state display module comprises a display unit and a feedback unit, the display unit is used for receiving the information transmitted by the control module and displaying the information on the LED display screen, and the LED display screen is provided with red, yellow and green signal lamps which are respectively used for representing the current fault, the potential fault and the normal state; the feedback unit is used for receiving feedback information of the fault and transmitting the feedback information to the control module.
Furthermore, the information transmission module comprises an industrial Ethernet and an Ethernet switch, and the data acquisition module, the fault detection module and the state display module are respectively connected with the control module through the industrial Ethernet by utilizing the Ethernet switch.
Compared with the prior art, the invention has the beneficial effects that:
1. the intelligent spinning roving fault detection system provided by the invention can timely find corresponding faults and automatically detect and classify the faults by monitoring the vibration state of the roller in real time, so that related personnel can timely maintain the intelligent spinning roving fault detection system, and the influence caused by the faults is reduced; meanwhile, the invention also predicts the potential fault through reasonably predicting the vibration state of the roller which normally operates at present, and is convenient for processing in advance, thereby reducing the fault occurrence rate and improving the safety and reliability of the roving process.
2. The invention adopts the sensor and the image acquisition device to respectively monitor the vibration signal and the image signal of the roller in real time, and carries out wavelet analysis on the vibration signal and edge detection on the image signal, thereby more comprehensively and accurately extracting corresponding fault characteristics and improving the accuracy of fault detection; the invention also utilizes the neural network to automatically classify the extracted characteristics, so that related personnel can know the fault reason in time, the maintenance speed is accelerated, and the influence of the fault on the roving process and the product quality is reduced as much as possible.
3. According to the method, the curve fitting method is adopted to predict the faults of the rollers classified as normal, the regression fitting is carried out on the characteristic parameters of the vibration state of the rollers to obtain the corresponding prediction curve, the future operation state of the rollers is reasonably predicted, the potential faults are conveniently predicted, measures are taken in advance, and the fault rate is effectively reduced.
Drawings
FIG. 1 is a schematic diagram of a configuration of an intelligent spun roving fault detection system of the present invention;
fig. 2 is a main flow chart of the intelligent spinning roving fault detection system in use.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
Examples
Referring to fig. 1, an embodiment of the present invention provides an intelligent spinning roving fault detection system, including a data acquisition module, a control module, a fault detection module, a status display module and an information transmission module, where the control module is connected to the data acquisition module, the fault detection module and the status display module through the information transmission module, respectively; the data acquisition module comprises a vibration signal acquisition unit and an image signal acquisition unit which are respectively used for acquiring vibration data and image signals of the roller in the roving frame in real time; the fault detection module comprises a feature extraction unit, a fault classification unit and a fault prediction unit, wherein the input end of the feature extraction unit is connected with the control module, the output end of the feature extraction unit is connected with the input end of the fault classification unit, the output end of the fault classification unit is respectively connected with the control module and the input end of the fault prediction unit, and the output end of the fault prediction unit is connected with the control module.
The vibration signal acquisition unit comprises a plurality of eddy current sensors, the eddy current sensors are arranged on the front side of the roller, the horizontal diameter of the roller penetrates through the center of the eddy current sensor probe, and the vibration signal acquisition unit is used for acquiring vibration data of the roller and transmitting the vibration data to the control module.
The image signal acquisition unit comprises a plurality of image acquisition devices consisting of industrial cameras and LED lamps, the image acquisition devices are arranged above the rollers, the vertical diameters of the rollers penetrate through the centers of lenses of the industrial cameras in the image acquisition devices, and the images are used for acquiring images when the rollers vibrate and are transmitted to the control module.
The control module comprises a signal processing unit, an image processing unit, a main control unit and a storage unit, wherein the signal processing unit comprises a signal amplifier and an A/D converter, the input end of the signal amplifier is connected with the vibration signal acquisition unit, the output end of the signal amplifier is connected with the input end of the A/D converter, and the output end of the A/D converter is connected with the main control unit; the image processing unit is used for carrying out gray level processing and binaryzation on an input image, reducing the image memory and improving the subsequent processing speed, the input end of the image processing unit is connected with the image signal acquisition unit, and the output end of the image processing unit is connected with the main control unit; the main control unit is also connected with the storage unit, the fault detection module and the state display module respectively. The signal amplifier is used for receiving and amplifying information transmitted by the data acquisition module, the A/D converter is used for converting received analog signals into digital signals, the main control unit is used for controlling the receiving and transmission of signals, and the storage unit is used for receiving and storing fault information for relevant personnel to check and export.
The characteristic extraction unit comprises a wavelet analysis layer, an image analysis layer and a characteristic vector extraction layer, wherein the wavelet analysis layer is used for carrying out wavelet transformation and orthogonal wavelet decomposition on the received vibration signals and inputting the vibration signals of each frequency band obtained after decomposition into the characteristic vector extraction layer; the image analysis layer is used for carrying out edge detection and amplitude calculation on the received image signal and inputting the result into the feature vector extraction layer; the characteristic vector extraction layer is used for arranging the energy values of the received vibration signals according to a scale sequence, forming a characteristic vector together with the amplitude value input by the image analysis layer and outputting the characteristic vector to the fault classification unit.
The fault classification unit comprises a memory, a training layer and a neural network classification layer, the memory is used for storing historical fault data of the roller, the training layer conducts neural network training based on the historical fault data in the memory, the neural network classification layer conducts fault classification on input feature vectors based on the trained neural network, and fault classification results are output to the fault prediction unit and the control module.
The fault classification result comprises normal fault, roller bending fault, roller eccentric fault, roller gear defect and roller gear rotation unbalance.
The fault prediction unit comprises a data extraction layer, a curve fitting layer and a result prediction layer, wherein the data extraction layer is used for receiving a fault classification result, periodically extracting data with normal fault classification results according to a fixed frequency, and inputting the extracted data into the curve fitting layer; the curve fitting layer is used for performing curve fitting on the data to obtain a prediction curve and outputting the prediction curve to the result prediction layer; and the result prediction layer is used for matching the prediction curve with a set threshold value and outputting the prediction result to the control module.
The state display module comprises a display unit and a feedback unit, the display unit is used for receiving the information transmitted by the control module and displaying the information on the LED display screen, and the LED display screen is provided with red, yellow and green signal lamps which are respectively used for representing the current fault, the potential fault and the normal state; the feedback unit is used for receiving feedback information of the fault and transmitting the feedback information to the control module.
The information transmission module comprises an industrial Ethernet and an Ethernet switch, and the data acquisition module, the fault detection module and the state display module are respectively connected with the control module through the industrial Ethernet by utilizing the Ethernet switch.
With reference to fig. 2, when the intelligent spinning roving fault detection system provided by the embodiment of the invention is in use, the eddy current sensor and the image acquisition device are respectively installed on each roller of the roving frame, so that the vibration signal and the image signal of the roller are acquired in real time and are respectively transmitted to the signal processing unit and the image processing unit in the control module through the industrial ethernet, the signal processing unit performs signal amplification and analog-to-digital conversion on the acquired vibration signal, and the image processing unit performs gray processing and binarization on the acquired image signal, so that the image memory is reduced, and the subsequent processing speed is increased; the processed vibration signal and the processed image signal are transmitted to a feature extraction unit in the fault detection module through the main control unit for feature extraction, and the method mainly comprises the following steps:
s11, discrete wavelet transform is carried out on the input roller vibration signal through a wavelet analysis layer to obtain a low-frequency time domain signal, Fourier transform is carried out on the time domain signal to obtain a frequency spectrum signal, orthogonal wavelet decomposition is carried out on the high-frequency band signal in the frequency spectrum signal to obtain nine-band vibration signal, and wavelet decomposition coefficients are respectively expressed as q1～q9Inputting the feature vector into a feature vector extraction layer;
s12, filtering the input image signal through the image analysis layer, and obtaining a gradient | G | of each pixel (x, y) in the image f (x, y), wherein the calculation formula is as follows:
wherein the content of the first and second substances,
then, a non-maximum value inhibition method is adopted to exclude pixel points with local gradient amplitude being non-maximum values, so that image edges are extracted, and an amplitude value A of the roller is obtained according to the distance between the image edges;
s13, calculating the vibration energy of the nine vibration signals input in the step S11 in the feature vector extraction layer, wherein the calculation formula is as follows:
in the formula, EjRepresenting the vibration energy of the j-th vibration signal, qjRepresents the j-th wavelet decomposition coefficient, and m represents qjThe number of the medium components;
s14, normalizing the vibration energy of the nine vibration signals, arranging them in order of scale, arranging the amplitude value a input in step S12 at the end, and forming a feature vector T (E) together1,E2,…,E9And a) and outputs it to the fault classification unit.
The fault classification unit comprises a memory, a training layer and a neural network classification layer, a large amount of historical fault data are prestored in the memory before detection is started, the historical fault data are derived from the fault situation accumulated in the past by the same roving frame, and the historical fault data comprise fault feature vectors and corresponding output vectors; and the training layer performs neural network training based on the historical fault data and stores the trained threshold value.
And after receiving the feature vectors, the fault classification unit inputs the feature vectors into a neural network classification layer, performs fault classification on the input feature vectors through a trained neural network, and judges a fault classification result according to the output vectors, wherein the output vectors are (1,0,0,0,0, 0), (0,1,0,0,0), (0,0,1,0,0), (0,0,0,1,0, 0,0,0,1,0) and (0,0,0,0, 0,1) respectively corresponding to normal, roller bending fault, roller eccentric fault, roller gear defect and roller gear rotation imbalance.
When the fault classification result is a roller bending fault, a roller eccentric fault, a roller gear defect or roller gear rotation unbalance, outputting corresponding fault information to the control module, reading the fault information by the control module, transmitting a signal to the state display module, controlling a red signal lamp on the LED display screen to be lighted, and displaying a fault source and fault classification on the LED display screen, so that related personnel can conveniently process the fault in time; meanwhile, the fault information input into the control module is filed in the storage unit for relevant personnel to check and export, so that the summary analysis is facilitated.
When the fault classification result is normal, outputting the corresponding fault information to a fault prediction unit for predicting the fault information, and mainly comprising the following steps of:
s21, receiving the fault information through the data extraction layer, identifying output vectors therein, extracting the output vectors according to a group of frequencies per hour, fitting the input curves of 24 groups of output vectors after extracting the output vectors, wherein the 24 groups of output vectors are respectively matched by X1～X24Is shown, in which:
Xi＝(ai,bi,ci,di,ei)；
s22, respectively making a in the curve fitting layeri,bi,ci,di,eiRespectively fitting the function curves relative to the time into five prediction curves by a least square method, predicting numerical values of each vector parameter 24 hours later, and outputting the numerical values to a result prediction layer;
s23, in the result prediction layer, a is setiHas a threshold value of 0.98-1.02, bi,ci,di,eiThe threshold range of (a) is 0-0.02, the five prediction curves are respectively matched, when the numerical values in the five prediction curves are all in the threshold range, the prediction result is normal, and when b is in the threshold rangei,ci,di,eiAnd when the corresponding prediction curve exceeds the threshold range, the prediction result respectively corresponds to the roller bending fault, the roller eccentric fault, the roller gear defect and the roller gear rotation unbalance.
The prediction result is output to the control module through the result prediction layer, the signal is transmitted to the state display module after being read by the control module, and when the prediction result is normal, the control module controls a green signal lamp on the LED display screen to be turned on and displays the normal signal on the LED display screen; when the prediction result is a roller bending fault, a roller eccentric fault, a roller gear defect or roller gear rotation unbalance, the control module controls a yellow signal lamp on the LED display screen to light, and displays the types of potential faults and corresponding faults and the predicted occurrence time on the LED display screen, so that related personnel can conveniently process the faults in time.
After the fault classification result and the fault detection result are displayed through the state display module, relevant workers can check and process corresponding faults; meanwhile, related workers can also perform fault feedback through a feedback interface on the display screen, the fault feedback comprises processed faults, fault error reporting and fault omission, and the feedback information is received through the feedback unit and transmitted to the storage unit through the control module for statistical analysis.
Through the mode, the intelligent spinning roving fault detection system provided by the invention can realize detection, classification and prediction of roller faults in the roving process, reduces the influence caused by the faults and the roller fault rate, and improves the safety and reliability of the roving process.
The above description is only for the purpose of illustrating the technical solutions of the present invention and is not intended to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; all the equivalent structures or equivalent processes performed by using the contents of the specification and the drawings of the invention, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The utility model provides an intelligence spinning roving fault detection system which characterized in that: the intelligent control system comprises a data acquisition module, a control module, a fault detection module, a state display module and an information transmission module, wherein the control module is respectively connected with the data acquisition module, the fault detection module and the state display module through the information transmission module; the data acquisition module comprises a vibration signal acquisition unit and an image signal acquisition unit which are respectively used for acquiring vibration data and image signals of the roller in the roving frame in real time; the fault detection module comprises a feature extraction unit, a fault classification unit and a fault prediction unit, wherein the input end of the feature extraction unit is connected with the control module, the output end of the feature extraction unit is connected with the input end of the fault classification unit, the output end of the fault classification unit is respectively connected with the control module and the input end of the fault prediction unit, and the output end of the fault prediction unit is connected with the control module.
2. The intelligent spinning roving fault detection system of claim 1, wherein: the vibration signal acquisition unit comprises a plurality of eddy current sensors, the eddy current sensors are arranged on the front side of the roller, the horizontal diameter of the roller penetrates through the center of the eddy current sensor probe, and the vibration signal acquisition unit is used for acquiring vibration data of the roller and transmitting the vibration data to the control module.
3. The intelligent spinning roving fault detection system of claim 1, wherein: the image signal acquisition unit comprises a plurality of image acquisition devices consisting of industrial cameras and LED lamps, the image acquisition devices are arranged above the rollers, the vertical diameters of the rollers penetrate through the centers of lenses of the industrial cameras in the image acquisition devices, and the images are used for acquiring images when the rollers vibrate and are transmitted to the control module.
4. The intelligent spinning roving fault detection system of claim 1, wherein: the control module comprises a signal processing unit, an image processing unit, a main control unit and a storage unit, wherein the signal processing unit comprises a signal amplifier and an A/D converter, the input end of the signal amplifier is connected with the vibration signal acquisition unit, the output end of the signal amplifier is connected with the input end of the A/D converter, and the output end of the A/D converter is connected with the main control unit; the image processing unit is used for carrying out gray level processing and binaryzation on an input image, reducing the image memory and improving the subsequent processing speed, the input end of the image processing unit is connected with the image signal acquisition unit, and the output end of the image processing unit is connected with the main control unit; the main control unit is also connected with the storage unit, the fault detection module and the state display module respectively.
5. The intelligent spinning roving fault detection system of claim 1, wherein: the characteristic extraction unit comprises a wavelet analysis layer, an image analysis layer and a characteristic vector extraction layer, wherein the wavelet analysis layer is used for carrying out wavelet transformation and orthogonal wavelet decomposition on the received vibration signals and inputting the vibration signals of each frequency band obtained after decomposition into the characteristic vector extraction layer; the image analysis layer is used for carrying out edge detection and amplitude calculation on the received image signal and inputting the result into the feature vector extraction layer; the characteristic vector extraction layer is used for arranging the energy values of the received vibration signals according to a scale sequence, forming a characteristic vector together with the amplitude value input by the image analysis layer and outputting the characteristic vector to the fault classification unit.
6. The intelligent spinning roving fault detection system of claim 5, wherein: the fault classification unit comprises a memory, a training layer and a neural network classification layer, the memory is used for storing historical fault data of the roller, the training layer conducts neural network training based on the historical fault data in the memory, the neural network classification layer conducts fault classification on input feature vectors based on the trained neural network, and fault classification results are output to the fault prediction unit and the control module.
7. The intelligent spinning roving fault detection system of claim 6, wherein: the fault classification result comprises normal fault, roller bending fault, roller eccentric fault, roller gear defect and roller gear rotation unbalance.
8. The intelligent spinning roving fault detection system of claim 7, wherein: the fault prediction unit comprises a data extraction layer, a curve fitting layer and a result prediction layer, wherein the data extraction layer is used for receiving a fault classification result, periodically extracting data with normal fault classification results according to a fixed frequency, and inputting the extracted data into the curve fitting layer; the curve fitting layer is used for performing curve fitting on the data to obtain a prediction curve and outputting the prediction curve to the result prediction layer; and the result prediction layer is used for matching the prediction curve with a set threshold value and outputting the prediction result to the control module.
9. The intelligent spinning roving fault detection system of claim 1, wherein: the state display module comprises a display unit and a feedback unit, the display unit is used for receiving the information transmitted by the control module and displaying the information on the LED display screen, and the LED display screen is provided with red, yellow and green signal lamps which are respectively used for representing the current fault, the potential fault and the normal state; the feedback unit is used for receiving feedback information of the fault and transmitting the feedback information to the control module.
10. The intelligent spinning roving fault detection system of claim 1, wherein: the information transmission module comprises an industrial Ethernet and an Ethernet switch, and the data acquisition module, the fault detection module and the state display module are respectively connected with the control module through the industrial Ethernet by utilizing the Ethernet switch.
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| CN116340875A (en) * | 2023-05-30 | 2023-06-27 | 单县鑫和纺织有限公司 | Roving frame operation fault prediction system based on data analysis |
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| CN116340875A (en) * | 2023-05-30 | 2023-06-27 | 单县鑫和纺织有限公司 | Roving frame operation fault prediction system based on data analysis |
| CN116340875B (en) * | 2023-05-30 | 2023-10-17 | 单县鑫和纺织有限公司 | Roving frame operation fault prediction system based on data analysis |
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Application publication date: 20201002 |