Intelligent fault detection system for methanol engine

Abstract

The application discloses a kind of methanol engine fault intelligent detection systems, it is related to engine fault detection field, including: vehicle speed monitoring module, for real-time monitoring vehicle speed information, based on to vehicle speed information is stored;Detection module, for real-time acquisition vehicle engine infrared thermal imaging image and vibration signal, to infrared thermal imaging image and vibration signal are stored;The present application fuses infrared thermal imaging and vibration signal, detects engine state comprehensively and accurately, by the comprehensive consideration of speed, vehicle balance and other aspects of information, can filter out more representative detection data under complex working conditions, greatly improve the accuracy of detection, in the fault risk analysis link, using unique algorithm calculates reference value and risk value, also according to vehicle speed intelligent adjustment weight coefficient, make fault risk assessment more close to actual situation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engine fault detection, in particular to a methanol engine fault intelligent detection system. BACKGROUND

[0002] Methanol engine has certain advantages in the field of automobile application. It burns cleanly, can reduce harmful gas emissions, and the source of methanol is extensive and the cost is relatively low. In addition, the power performance of methanol engine is good, which can be comparable to traditional gasoline engine, and has been applied to some extent in some areas.

[0003] The patent application with the application number 202010574693.0 discloses a methanol engine fault detection method, which comprises: collecting the speed of the methanol engine in real time; collecting the oil emulsification value of the methanol engine when the speed of the methanol engine is less than or equal to a first preset value; collecting the oil sump space gas pressure value of the methanol engine when the speed of the methanol engine is greater than or equal to a second preset value; judging whether the oil in the methanol engine is emulsified according to the oil emulsification value or the oil sump space gas pressure value; if yes, controlling the methanol engine to act accordingly according to the emulsification degree; the emulsification degree includes primary emulsification and secondary emulsification; and the step of controlling the methanol engine to act accordingly according to the emulsification degree comprises: judging whether the emulsification degree is primary emulsification or secondary emulsification; if the emulsification degree is primary emulsification, controlling the methanol engine to work within a preset power range; if the emulsification degree is secondary emulsification, controlling the methanol engine to stop working, which aims to solve the problem of how to find out the oil emulsification as soon as possible to prevent methanol engine failure.

[0004] However, when the methanol engine is applied to the scene of automobile power components, the probability of failure of the methanol engine during operation may change at any time with the operation of the vehicle driving user, which also makes it difficult for ordinary related fault detection technology to well compatible with fault detection during vehicle operation.

[0005] Therefore, a methanol engine fault intelligent detection system is proposed. SUMMARY

[0006] In view of the above shortcomings of the prior art, the present application provides a methanol engine fault intelligent detection system, which can effectively solve the problems of the prior art.

[0007] To achieve the above purpose, the present application is realized by the following technical scheme;

[0008] The application discloses a kind of methanol engine fault intelligent detection system, comprising: vehicle speed monitoring module, for real-time monitoring vehicle speed information, based on to vehicle speed information is stored;Detection module, for real-time acquisition vehicle engine infrared thermal imaging image and vibration signal, to infrared thermal imaging image and vibration signal are stored;Analysis module, for obtaining infrared thermal imaging image and vibration signal in monitoring module, based on infrared thermal imaging image and vibration information analysis methanol engine fault risk;Determination module, for setting methanol engine fault determination threshold, receives methanol engine fault risk determination result in analysis module, based on determination result and engine fault determination threshold comparison, determine whether methanol engine exists fault;Prediction module, for traversing analysis module historical methanol engine fault analysis result, based on analysis result traversal, predict whether methanol engine exists fault tendency.

[0009] Further, the vehicle speed monitoring module is provided with a sensing unit inside, the sensing unit is used for real-time sensing vehicle balance information, storing balance information, real-time determining whether the vehicle is currently on flat road, the sensing unit and the operating frequency of vehicle speed monitoring module are same;

[0010] Wherein, the sensing unit synchronously executes determination of whether the vehicle is currently on flat road once every time sensing vehicle balance information, the sensing unit is integrated by four laser ranging sensors, based on top plane perspective is recorded as left upper, left lower, right upper, right lower laser ranging sensor, four laser ranging sensors are synchronously operated, and the distance information perceived is vehicle balance information perceived by sensing unit.

[0011] Further, the sensing unit is arranged on the surface of vehicle chassis, and the ranging target is the distance from itself to the road surface, and the determination logic of whether the vehicle is on flat road in the sensing unit is represented as:

[0012] ;

[0013] In the formula: It is determination value; It is laser ranging sensor deployment amount; It is the ranging result of the i th laser ranging sensor and the i+1 th laser ranging sensor; It is preset ranging error threshold value for determining vehicle inclination; It is determination function;

[0014] Wherein, determination function Value is subject to: If true, then =1, If false, then =0, based on formula (1) calculation, the determination value obtained is When formula (2) is established, it is determined that the vehicle is on a flat road, otherwise, it is determined that the vehicle is not on a flat road.

[0015] Further, the detection module performs a storage operation stage, and the collected infrared thermal imaging images and vibration signals are stored separately.

[0016] The monitoring module performs a vehicle engine infrared thermal imaging image and vibration signal collection stage, synchronously obtains the determination result of whether the vehicle is on a flat road in the vehicle speed monitoring module, performs a collection operation of the vehicle engine infrared thermal imaging image and vibration signal in the time domain when the determination result is yes, synchronously obtains the vehicle speed information monitored by the vehicle speed monitoring module in the corresponding time domain based on the collection time domain of the vehicle engine infrared thermal imaging image and vibration signal, analyzes the complexity of the vehicle working condition based on the vehicle speed information, and filters the commercial vehicle engine infrared thermal imaging image and vibration signal from the collected vehicle engine infrared thermal imaging image and vibration signal based on the complexity analysis result of the vehicle working condition.

[0017] Further, the complexity analysis logic of the vehicle working condition is represented as:

[0018] The vehicle speed information is sorted based on time sequence, each adjacent three vehicle speed information is taken as a group to perform the complexity analysis of the vehicle working condition.

[0019] ;

[0020] In the formula, is the complexity of the vehicle working condition; is the qth and q+1th vehicle speed; is the angle between the qth and q+1th vehicle speed representation line segment and the horizontal axis in the vehicle speed information broken line graph, and the angle between the q+1th and q+2th vehicle speed representation line segment and the horizontal axis in the vehicle speed information broken line graph;

[0021] wherein, The greater the value is, the more complex the vehicle working condition is. The vehicle speed information is stored in the form of a broken line graph, the horizontal axis of the broken line graph represents time, and the vertical axis represents vehicle speed. A user of the system end customizes a vehicle working condition complexity determination threshold value, compares the complexity calculation result of the vehicle working condition based on each group of vehicle speed information with the vehicle working condition complexity determination threshold value, determines the vehicle speed information of the vehicle working condition complexity, and records the vehicle engine infrared thermal imaging image and vibration signal to which the vehicle speed information other than the corresponding vehicle speed information of the vehicle working condition complexity determination result belongs as a screening result of the commercial vehicle engine infrared thermal imaging image and vibration signal.

[0022] Further, the infrared thermal imaging image and vibration signal obtained by the analysis module is the commercial vehicle engine infrared thermal imaging image and vibration signal screened.

[0023] The analysis module is provided with a classification unit and a recording unit, the classification unit is used for classifying the infrared thermal imaging images and the vibration signals, and re-classifying according to vehicle speed information of the infrared thermal imaging images and the vibration signals, so that the infrared thermal imaging images or the vibration signals corresponding to the same vehicle speed information are classified into one category, and the recording unit is used for recording a methanol engine fault risk analysis result of each operation of the analysis module.

[0024] Further, the methanol engine fault risk analysis logic in the analysis module is represented as:

[0025] ;

[0026] In the formula: is a methanol engine fault risk reference value; is a total amount of infrared thermal imaging images; is a difference between the jth infrared thermal imaging image and the j+1th infrared thermal imaging image; is a total amount of vibration signal frequency spectrums; is a difference between the vth frequency spectrum and the v+1th frequency spectrum; is a methanol engine fault risk; is a total amount of methanol engine fault risk reference values; is a weight coefficient;

[0027] In the formula, the vibration signal is represented in the form of a frequency spectrum, is calculated based on a group of infrared thermal imaging images and a group of vibration signals, and the infrared thermal imaging images and the vibration signals correspond to the same vehicle speed information, the methanol engine fault risk reference value is calculated based on each pair of infrared thermal imaging images and vibration signals corresponding to the same vehicle speed information, and a methanol engine fault risk reference value set is obtained, that is Finally, the methanol engine fault risk is calculated as , The greater the methanol engine fault risk is, the higher the methanol engine fault risk is.

[0028] Further, the is calculated based on a color distribution difference algorithm, and the is calculated based on a Euclidean distance algorithm.

[0029] The weight coefficient obeys , and each weight coefficient is a positive number, and the weight coefficient also obeys a set logic that the faster the corresponding vehicle speed of the weight coefficient product object is, the greater the weight coefficient value is in the calculation formula of .

[0030] Further, the prediction module triggers operation in the state that the determination module determines no for the latest three times in succession.

[0031] The prediction module obtains the latest three times of methanol engine fault risk analysis results analyzed by the analysis module in the recording unit, and if the latest three times of methanol engine fault analysis results show a continuous upward trend, it is predicted that the methanol engine has a fault tendency, otherwise, it is predicted that the methanol engine has no fault tendency.

[0032] Further, the vehicle speed monitoring module is internally connected with the sensing unit through medium electrical interaction, the vehicle speed monitoring module is connected with the detection module through wireless network interaction, the detection module is connected with the analysis module through wireless network interaction, the analysis module is connected with the classification unit and the recording unit through wireless network interaction, and the analysis module is connected with the determination module and the prediction module through wireless network interaction.

[0033] Compared with the known prior art, the technical scheme provided by the present application has the following beneficial effects:

[0034] The present application provides a kind of methanol engine fault intelligent detection system, which is in the process of operation, fusion infrared thermal imaging and vibration signal, engine state is detected comprehensively and accurately, by the comprehensive consideration of vehicle speed, vehicle balance and other aspects of information, can filter out more representative detection data under complex working conditions, greatly improve the accuracy of detection, in the fault risk analysis link, use unique algorithm to calculate reference value and risk value, also according to the weight coefficient of intelligent adjustment of vehicle speed, make fault risk assessment more in line with actual situation;

[0035] In addition, the system can not only determine whether the engine has a fault in real time, but also can predict the fault tendency based on historical analysis results when determining no fault for several times in succession, provide a scientific basis for maintenance in advance, effectively reduce the loss caused by engine failure, and ensure the safe and stable operation of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0037] Figure 1 It is a structural schematic diagram of a methanol engine fault intelligent detection system. DETAILED DESCRIPTION

[0038] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0039] The present application will be further described below in conjunction with the embodiments.

[0040] Embodiment:

[0041] As shown in the figure, the methanol engine fault intelligent detection system of the embodiment comprises: Figure 1

[0042] A vehicle speed monitoring module is configured to monitor vehicle speed information in real time and store the vehicle speed information.

[0043] It should be noted that the vehicle speed information is monitored by contact or non-contact when monitoring the vehicle speed information.

[0044] Contact: measured by physical connection, such as wheel speed sensor, transmission shaft speed sensor, and odometer soft shaft.

[0045] Non-contact: without physical contact, such as GPS / Beidou, laser radar, millimeter wave radar, and visual sensor. Traffic monitoring can also use differential Doppler radar for long-distance speed measurement.

[0046] The vehicle speed monitoring module is internally provided with a sensing unit, which is configured to sense vehicle balance information in real time, store the balance information, and determine in real time whether the vehicle is currently on a flat road. The sensing unit has the same operating frequency as the vehicle speed monitoring module.

[0047] The sensing unit synchronously performs determination of whether the vehicle is currently on a flat road each time the vehicle balance information is sensed. The sensing unit is integrated by four laser ranging sensors, which are denoted as left-up, left-down, right-up, and right-down laser ranging sensors based on a top-down plane perspective. The four laser ranging sensors operate synchronously, and the distance information sensed by the operation of the sensing unit is the vehicle balance information sensed by the operation of the sensing unit.

[0048] The sensing unit is arranged on the surface of the vehicle chassis. The ranging target is the distance from the sensing unit to the road surface. The determination logic of whether the vehicle is on a flat road in the sensing unit is as follows:

[0049] ;

[0050] In the formula, D is a determination value. ​​Deploying quantity for laser ranging sensor; Ranging result of the i-th laser ranging sensor and the i+1-th laser ranging sensor; Pre-set ranging error threshold for determining vehicle inclination; Determination function;

[0051] Wherein, the determination function The value is subject to: If true, =1, If false, =0, the determination value obtained by calculation based on formula (1) When formula (2) is true, it is determined that the vehicle is on a flat road, otherwise, it is determined that the vehicle is not on a flat road.

[0052] Through the calculation of the above logical formula, it is determined whether the vehicle is on a flat road, which is used as support for the subsequent screening of thermal imaging images and vibration signals in this embodiment.

[0053] The detection module is used to collect the infrared thermal imaging image and the vibration signal of the vehicle engine in real time, and store the infrared thermal imaging image and the vibration signal.

[0054] It should be noted that when collecting the infrared thermal imaging image and the vibration signal of the vehicle engine, they are collected by the following means respectively:

[0055] Infrared thermal imaging image collection: mainly uses infrared thermal imager, which is divided into portable handheld device and vehicle-mounted fixed type. The portable device is manually held to scan each part of the engine to capture the surface temperature distribution to generate a thermal imaging image, which is suitable for maintenance and detection scene; the vehicle-mounted fixed type system collects thermal images in real time during engine operation through the infrared camera integrated on the vehicle or detection equipment, analyzes the temperature abnormal area combined with data processing software, and is commonly used in automatic detection line or vehicle-mounted intelligent diagnosis system.

[0056] Vibration signal collection: relies on vibration sensors, which are installed on vibration sensitive parts such as engine cylinder, crankcase, bearing seat, etc. through magnetic attraction, bolt fixation or pasting method, and can sense acceleration, speed or displacement signals generated by mechanical vibration in real time. After the sensor converts the vibration signal into an electrical signal, the data acquisition device performs analog-to-digital conversion, and then transmits it to the analysis equipment through wired or wireless transmission, and extracts vibration characteristics by using frequency spectrum analysis, time-frequency analysis and other algorithms, which is used to judge engine wear, looseness, imbalance and other faults. The two types of signals are often used together to improve the accuracy of engine condition monitoring through multi-source data fusion.

[0057] The detection module performs the storage operation stage to store the collected infrared thermal imaging image and vibration signal separately;

[0058] The monitoring module executes the vehicle engine infrared thermal imaging image and vibration signal acquisition phase, and simultaneously obtains the determination result of the vehicle speed monitoring module on whether the vehicle is on a level road. In the time domain when the determination result is yes, the monitoring module performs the vehicle engine infrared thermal imaging image and vibration signal acquisition operation. Based on the acquisition time domain of the vehicle engine infrared thermal imaging image and vibration signal, the monitoring module synchronously acquires the vehicle speed information monitored by the speed monitoring module in the corresponding time domain. The complexity of the vehicle operating condition is analyzed based on the vehicle speed information. The commercial vehicle engine infrared thermal imaging image and vibration signal are screened from the acquired vehicle engine infrared thermal imaging image and vibration signal according to the vehicle operating condition complexity analysis result.

[0059] The logic of vehicle operating condition complexity analysis is expressed as:

[0060] Sorting vehicle speed information based on time sequence, and performing vehicle operating condition complexity analysis with every three adjacent vehicle speed information as a group;

[0061] ;

[0062] Where: The complexity of vehicle working conditions; is the speed of the qth and q+1th vehicles; The angles between the qth and q+1th vehicle speed representation line segments in the vehicle speed information line graph and the horizontal axis, and the angles between the q+1th and q+2th vehicle speed representation line segments in the vehicle speed information line graph and the horizontal axis;

[0063] in, The larger the value, the more complex the vehicle operating condition. The vehicle speed information is stored in the form of a broken line graph, with the horizontal axis of the broken line graph representing time and the vertical axis representing vehicle speed. The system user defines a threshold for determining vehicle operating condition complexity. The vehicle operating condition complexity calculation result based on each group of vehicle speed information is compared with the threshold for determining vehicle operating condition complexity to determine vehicle speed information with complex vehicle operating conditions. The infrared thermal imaging images and vibration signals of the vehicle engines belonging to the vehicle speed information other than the corresponding vehicle speed information with a determination result of complex vehicle operating conditions are recorded as the screening results of the infrared thermal imaging images and vibration signals of the commercial vehicle engines.

[0064] The complexity of the vehicle operating conditions is calculated through the above logical formula, which can be used as support for further screening of thermal imaging images and vibration signals, and more valuable thermal imaging images and vibration signal data can be selected to support the fault detection accuracy of methanol engines.

[0065] An analysis module is used to obtain infrared thermal imaging images and vibration signals in the monitoring module and analyze the failure risk of the methanol engine based on the infrared thermal imaging images and vibration information;

[0066] The infrared thermal imaging images and the vibration signals obtained by the analysis module are the available infrared thermal imaging images and the vibration signals of the vehicle engine screened;

[0067] The analysis module is provided with a classification unit and a recording unit. The classification unit is used for classifying the infrared thermal imaging images and the vibration signals, and re-classifying according to the vehicle speed information of the source vehicle, so that the infrared thermal imaging images or the vibration signals corresponding to the same vehicle speed information are classified into one category. The recording unit is used for recording the methanol engine fault risk analysis result of each operation of the analysis module.

[0068] The methanol engine fault risk analysis logic in the analysis module is represented as:

[0069]

[0070] In the formula: is the methanol engine fault risk reference value; is the total amount of infrared thermal imaging images; is the difference between the jth infrared thermal imaging image and the j+1th infrared thermal imaging image; is the total amount of vibration signal frequency spectrum; is the difference between the vth frequency spectrum and the v+1th frequency spectrum; is the methanol engine fault risk; is the total amount of methanol engine fault risk reference values; is the weight coefficient;

[0071] The vibration signal is represented in the form of frequency spectrum, The methanol engine fault risk reference value is calculated based on a set of infrared thermal imaging images and a set of vibration signals corresponding to the same vehicle speed information. The methanol engine fault risk reference value set is obtained by calculating the methanol engine fault risk reference value based on the infrared thermal imaging images and the vibration signals corresponding to the same vehicle speed information. The final methanol engine fault risk is calculated as , The greater the methanol engine fault risk is, the higher the methanol engine fault risk is.

[0072] The color distribution difference algorithm is used for calculation, The Euclidean distance algorithm is used for calculation;

[0073] The above logical formula provides a specified judgment logic for the methanol engine fault judgment of the system in this embodiment.

[0074] The weight coefficient obeys , and each weight coefficient is a positive number, and the weight coefficient is in the range of​ In the calculation formula, the weight coefficient is subject to the set logic that the faster the corresponding vehicle speed of the product of the weight coefficients is, the greater the value of the weight coefficient is;

[0075] The determination module is configured to set a methanol engine fault determination threshold, receive the methanol engine fault risk determination result in the analysis module, compare the determination result with the engine fault determination threshold, and determine whether the methanol engine has a fault.

[0076] The prediction module is configured to traverse the historical methanol engine fault analysis result in the analysis module, and predict whether the methanol engine has a fault tendency based on the analysis result.

[0077] The prediction module is triggered to operate when the latest three consecutive determination results of the determination module are negative.

[0078] The prediction module obtains the latest three methanol engine fault risk analysis results in the analysis module from the recording unit. If the latest three methanol engine fault analysis results show a continuous upward trend, it is predicted that the methanol engine has a fault tendency. Otherwise, it is predicted that the methanol engine does not have a fault tendency.

[0079] The speed monitoring module is connected to the sensing unit through medium electrical interaction, the speed monitoring module is connected to the detection module through wireless network interaction, the detection module is connected to the analysis module through wireless network interaction, the analysis module is connected to the classification unit and the recording unit through wireless network interaction, and the analysis module is connected to the determination module and the prediction module through wireless network interaction.

[0080] In this embodiment, the speed monitoring module operates to monitor the vehicle speed information in real time, stores the vehicle speed information, and synchronously senses the vehicle balance information in real time. The sensing unit stores the balance information and determines whether the vehicle is currently on a flat road in real time. The detection module operates to collect the infrared thermal imaging image and the vibration signal of the vehicle engine in real time, and stores the infrared thermal imaging image and the vibration signal. The analysis module further obtains the infrared thermal imaging image and the vibration signal in the monitoring module, analyzes the methanol engine fault risk based on the infrared thermal imaging image and the vibration information, and synchronously classifies the infrared thermal imaging image and the vibration signal. The classification unit synchronously classifies the infrared thermal imaging image and the vibration signal again according to the vehicle speed information of the source vehicle, so that the infrared thermal imaging image or the vibration signal corresponding to the same vehicle speed information is classified into one category. The recording unit records the methanol engine fault risk analysis result of each operation of the analysis module in real time. The determination module sets a methanol engine fault determination threshold, receives the methanol engine fault risk determination result in the analysis module, compares the determination result with the engine fault determination threshold, and determines whether the methanol engine has a fault. Finally, the prediction module traverses the historical methanol engine fault analysis result in the analysis module, and predicts whether the methanol engine has a fault tendency based on the analysis result.

[0081] Through the above-mentioned system in the embodiment, the methanol engine in the vehicle power assembly is detected for fault, the compatibility of the methanol engine running process is provided with real-time fault judgment and fault prediction, and the running safety and reliability of the methanol engine are effectively ensured.

[0082] In addition, the speed monitoring module of the detection system is internally provided with a sensing unit composed of four laser ranging sensors symmetrically arranged on the chassis of the vehicle, which measures the distance to the road in real time to determine whether it is on a flat road. The determination logic is: when the ranging difference of adjacent sensors ≤ preset threshold, the determination function is determined when the determination value P = 3 (i.e. any three adjacent sensors meet the condition), the vehicle is determined to be on a flat road, which provides a stable road condition basis for data collection.

[0083] The detection module only collects infrared thermal imaging images and vibration signals during the flat road period, and synchronously acquires the vehicle speed information in the corresponding time domain. The system analyzes the complexity of the working condition according to the vehicle speed time sequence grouping, and calculates the greater the angle difference of the polyline segment, the more complex the working condition. The signal data of simple working condition is screened out by user-defined threshold, which is classified and stored as effective detection data.

[0084] The analysis module analyzes the fault risk of the screened signal data. The infrared thermal imaging image sequence and the vibration signal spectrum sequence under the same vehicle speed are calculated by the color distribution difference algorithm and the Euclidean distance algorithm respectively, the adjacent difference is calculated, the single working condition fault risk reference value is generated, and the multi-working condition risk value is calculated by weighted summation. The sum of the weight coefficients is 1 and the faster the vehicle speed, the greater the weight. The prediction module is triggered when the determination module determines no fault for three times in a row. If the latest three fault risk values show a continuous upward trend, it is predicted that there is a fault tendency. Each module realizes data interaction through a wireless network to form a detection closed loop.

[0085] In summary, in the above-mentioned system in the embodiment, infrared thermal imaging and vibration signals are fused to comprehensively and accurately detect the engine state. Through comprehensive consideration of vehicle speed, vehicle balance and other information, more representative detection data can be screened out under complex working conditions, greatly improving the accuracy of detection. In the fault risk analysis link, unique algorithms are used to calculate reference values and risk values, and the weight coefficients are intelligently adjusted according to the vehicle speed, so that the fault risk evaluation is more in line with the actual situation. In addition, the system can not only determine in real time whether the engine has a fault, but also can predict the fault tendency based on historical analysis results when no fault is determined for several times in a row, providing a scientific basis for maintenance in advance, effectively reducing the loss caused by engine failure, and ensuring the safe and stable operation of the vehicle.

[0086] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A methanol engine fault intelligent detection system, characterized in that: include: The vehicle speed monitoring module is used to monitor the vehicle speed information in real time and store the vehicle speed information; The vehicle speed monitoring module is internally provided with a sensing unit, which is used to sense the vehicle balance information in real time, store the balance information, and determine in real time whether the vehicle is currently on a flat road. The sensing unit and the vehicle speed monitoring module operate at the same frequency; Each time the perception unit senses vehicle balance information, it simultaneously determines whether the vehicle is currently on a flat road. The perception unit is integrated with four laser ranging sensors, which are denoted as the upper left, lower left, upper right, and lower right laser ranging sensors based on a bird's-eye view. The four laser ranging sensors operate synchronously, and the distance information sensed by the operation is the vehicle balance information sensed by the perception unit. The sensing units are symmetrically deployed on the surface of the vehicle chassis. The distance measurement target is the distance between the vehicle and the road surface. The judgment logic of whether the vehicle is on a flat road in the sensing unit is expressed as follows: ； Where: is the judgment value; Deployment volume of laser ranging sensors; is the ranging result of the i-th laser ranging sensor and the i+1-th laser ranging sensor; is a preset ranging error threshold for determining vehicle tilt; is the decision function; Among them, the decision function The value is subject to: If established, =1, If not established, =0, calculated based on formula (1), the judgment value is When the equation (2) holds true, the vehicle is judged to be on a level road; otherwise, the vehicle is judged to be not on a level road. A detection module is used to collect infrared thermal imaging images and vibration signals of vehicle engines in real time and store them; The detection module performs a storage operation phase to distinguish and store the collected infrared thermal imaging images and vibration signals; The monitoring module executes the vehicle engine infrared thermal imaging image and vibration signal acquisition phase, synchronously obtains the determination result of the vehicle speed monitoring module on whether the vehicle is on a level road, executes the vehicle engine infrared thermal imaging image and vibration signal acquisition operation in the time domain when the determination result is yes, synchronously obtains the vehicle speed information monitored by the vehicle speed monitoring module in the corresponding time domain based on the acquisition time domain of the vehicle engine infrared thermal imaging image and vibration signal, analyzes the complexity of the vehicle operating condition based on the vehicle speed information, and screens the commercial vehicle engine infrared thermal imaging image and vibration signal from the acquired vehicle engine infrared thermal imaging image and vibration signal according to the vehicle operating condition complexity analysis result; The vehicle operating condition complexity analysis logic is expressed as: Sorting vehicle speed information based on time sequence, and performing vehicle operating condition complexity analysis with every three adjacent vehicle speed information as a group; ； Where: The complexity of vehicle working conditions; is the speed of the qth and q+1th vehicles; The angles between the qth and q+1th vehicle speed representation line segments in the vehicle speed information line graph and the horizontal axis, and the angles between the q+1th and q+2th vehicle speed representation line segments in the vehicle speed information line graph and the horizontal axis; in, The larger the value, the more complex the vehicle operating condition. The vehicle speed information is stored in the form of a broken line graph, with the horizontal axis of the broken line graph representing time and the vertical axis representing vehicle speed. The system user defines a threshold for determining vehicle operating condition complexity. The vehicle operating condition complexity calculation result based on each group of vehicle speed information is compared with the threshold for determining vehicle operating condition complexity to determine vehicle speed information with complex vehicle operating conditions. The infrared thermal imaging images and vibration signals of the vehicle engines belonging to the vehicle speed information other than the corresponding vehicle speed information with a determination result of complex vehicle operating conditions are recorded as the screening results of the infrared thermal imaging images and vibration signals of the commercial vehicle engines. An analysis module is used to obtain infrared thermal imaging images and vibration signals in the monitoring module and analyze the failure risk of the methanol engine based on the infrared thermal imaging images and vibration information; The infrared thermal imaging image and vibration signal acquired by the analysis module are the infrared thermal imaging image and vibration signal of the commercial vehicle engine obtained by screening; The analysis module is provided with a classification unit and a recording unit at a lower level. The classification unit is used to classify the infrared thermal imaging image and the vibration signal, and simultaneously reclassify the infrared thermal imaging image and the vibration signal according to the vehicle speed information of the vehicle source, so that the infrared thermal imaging image or the vibration signal with the same vehicle speed information is classified into the same category. The recording unit is used to record the methanol engine fault risk analysis results of each operation of the analysis module; a determination module, configured to set a methanol engine fault determination threshold, receive the methanol engine fault risk determination result from the analysis module, and determine whether the methanol engine has a fault based on a comparison of the determination result with the engine fault determination threshold; The prediction module is used to traverse the historical methanol engine fault analysis results of the analysis module and predict whether the methanol engine has a fault tendency based on the traversal of the analysis results.

2. The intelligent methanol engine fault detection system according to claim 1, characterized in that: The methanol engine failure risk analysis logic in the analysis module is expressed as follows: ； Where: is the reference value for methanol engine failure risk; is the total amount of infrared thermal imaging images; is the difference between the jth infrared thermal imaging image and the j+1th infrared thermal imaging image; The total amount of spectrum representing the vibration signal; is the difference between the spectrum of the vth segment and the spectrum of the v+1th segment; The risk of methanol engine failure; is the total amount of the reference value for methanol engine failure risk; is the weight coefficient; Among them, the vibration signal is expressed in the form of spectrum. Based on a set of infrared thermal imaging images and a set of vibration signals, and the infrared thermal imaging images and vibration signals correspond to the same vehicle speed information, the methanol engine failure risk reference value is obtained based on the infrared thermal imaging images and vibration signals corresponding to the same vehicle speed information, and the methanol engine failure risk reference value set is obtained, that is, , and finally obtain , The larger the value, the higher the risk of methanol engine failure.

3. The intelligent methanol engine fault detection system according to claim 2, characterized in that: described Calculation is performed based on the color distribution difference algorithm, Calculation is based on the Euclidean distance algorithm; The weight coefficient obey , and each weight coefficient is a positive number, and the weight coefficient is The calculation formula also follows the setting logic that the faster the vehicle speed corresponding to the weight coefficient product object is, the larger the weight coefficient value is.

4. The intelligent methanol engine fault detection system according to claim 1, characterized in that: The prediction module is triggered to run when the judgment module's latest three consecutive judgment results are negative: The prediction module obtains the three latest methanol engine failure risk analysis results analyzed by the analysis module in the recording unit. If the three latest methanol engine failure analysis results show a continuous upward trend, it is predicted that the methanol engine has a failure tendency; otherwise, it does not have a failure tendency.

5. The intelligent fault detection system for a methanol engine according to claim 1, characterized in that: The vehicle speed monitoring module is electrically connected to a perception unit through a medium, the vehicle speed monitoring module is interactively connected to a detection module through a wireless network, the detection module is interactively connected to an analysis module through a wireless network, the analysis module is interactively connected to a classification unit and a recording unit through a wireless network, and the analysis module is interactively connected to a determination module and a prediction module through a wireless network.

Images