CN117507824A - Information system for intelligent early warning and analysis of motor faults - Google Patents
Information system for intelligent early warning and analysis of motor faults Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0061—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electrical machines
Abstract
The invention discloses an intelligent early warning and analyzing information system for motor faults, which relates to the technical field of motor analysis, wherein a prediction analysis module is started when judging that a vehicle is located on an expressway, generates a state coefficient for the motor after comprehensive calculation of electricity data and mechanical data, analyzes the running state of the motor according to the comparison result of the state coefficient and a gradient early warning threshold value, reduces the highest running speed of a current vehicle when the motor of the predicted vehicle supports the vehicle to continue running and has slight abnormality, and sends a calculation result to a driver, and if the highest running speed of the calculated current vehicle is lower than the lowest speed limit of the expressway, or the motor of the predicted vehicle does not support the vehicle to continue running, sends a second warning signal to the driver to remind the driver of needing to drive the vehicle to enter an emergency lane. The system can predict the faults of the motor and reasonably prompt the faults when the electric automobile runs on the expressway, and the safety of the electric automobile is greatly improved.
Description
Technical Field
The invention relates to the technical field of motor analysis, in particular to an intelligent early warning and analysis information system for motor faults.
Background
The development of electric vehicles benefits from the increased global concern for environmental protection and sustainability, and traditional internal combustion engine vehicles use fossil fuels, emit waste gas and greenhouse gases, have negative effects on the atmosphere and the environment, adopt clean energy sources, reduce exhaust emission, and are vital for coping with climate change and improving air quality;
the rise of the electric automobile benefits from the remarkable improvement of the battery technology, along with the development of the lithium ion battery technology, the energy density of the battery is improved, the charging time is shortened, the endurance mileage is increased, so that the electric automobile is attractive, in addition, the cost of the battery is continuously reduced, and the popularization of the electric automobile is promoted.
The prior art has the following defects:
the electric automobile usually shows excellent performance in a low-speed range, and can normally provide stable acceleration at low speed because the motor provides high torque, when the electric automobile is positioned on a highway and runs at high load because the motor is fast, the existing analysis system usually detects faults of the motor, and the motor is warned by the central console when the motor breaks down, however, when the motor breaks down, the electric automobile can possibly appear a "groveling pit" phenomenon, namely the electric automobile is stopped, and at the moment, if the electric automobile stops on a fast lane, rear-end collision and other safety accidents are extremely easy to be caused, and a large potential safety hazard exists;
therefore, the intelligent early warning and analyzing information system for the motor faults can predict and early warn the motor faults when the electric automobile runs at a high speed, so that a driver can drive the electric automobile into an emergency lane in advance before the motor faults, and the running safety of the electric automobile on a highway is greatly improved.
Disclosure of Invention
The invention aims to provide an intelligent early warning and analysis information system for motor faults, which aims to solve the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: an intelligent early warning and analyzing information system for motor faults comprises a driving road judging module, a first warning module, a data acquisition module, a prediction analysis module, an intelligent reminding module and a second warning module;
and a driving road judging module: judging whether the vehicle is on the expressway or not based on the shooting of a vehicle navigation system or a vehicle camera;
the first warning module: the vehicle speed warning device is used for warning the running speed range of the vehicle;
and a data acquisition module: during the running process of the vehicle, collecting the electricity data and the mechanical data of the motor, and preprocessing the electricity data and the mechanical data;
the prediction analysis module: starting when the vehicle is judged to be positioned on a highway and running, generating a state coefficient for the motor after comprehensively calculating electricity data and mechanical data, and analyzing the running state of the motor according to the comparison result of the state coefficient and the gradient early warning threshold value;
and the intelligent reminding module is used for: when the predicted vehicle motor supports the vehicle to continue running and slight abnormality exists, intelligently calculating to reduce the highest running speed of the current vehicle, and sending a calculation result to a driver;
the second warning module: and if the calculated highest running speed of the current vehicle is lower than the lowest speed limit of the expressway, or the motor of the vehicle is predicted to not support the vehicle to continue running, sending a second warning signal to the driver to remind the driver of needing to drive the vehicle to enter an emergency lane.
In a preferred embodiment, the data acquisition module acquires electrical data of the motor during vehicle travel, the electrical data including a coil magnetic field floating coefficient, and mechanical data including vibration amplitude, noise decibels, and rotational speed fluctuation amplitude.
In a preferred embodiment, the predictive analysis module calculates the state coefficient zt from the coil magnetic field floating coefficient, vibration amplitude, noise decibel and rotational speed fluctuation amplitude x The expression is:
wherein zd is vibration amplitude, zf is noise decibel, xc is coil magnetic field floating coefficient, zb is rotation speed fluctuation amplitude, alpha, beta, gamma and omega are respectively vibration amplitude, noise decibel, coil magnetic field floating coefficient and rotation speed fluctuation amplitude proportionality coefficient, and alpha, beta, gamma and omega are all more than 0.
In a preferred embodiment, the coil magnetic field floating coefficient is calculated as:
wherein xc is the coil magnetic field floating coefficient, C (t) is the magnetic field intensity variation of the motor coil during the running process of the electric automobile, [ t ] x ，t y ]For the period of early warning of current fluctuation, [ t ] i ，t j ]And the time period of early warning for voltage fluctuation.
In a preferred embodiment, the rotational speed fluctuation amplitude is calculated as:
wherein zb is the amplitude of rotation speed fluctuation, Z Real world y Z is the actual rotation speed of the motor at the y-th sampling point in the T time Flat plate avg For the average rotational speed of the motor in time T, y=1, 2, 3, 4, … …, m is the number of sampling points of the actual rotational speed.
In a preferred embodiment, the gradient early-warning threshold includes a first early-warning threshold and a second early-warning threshold, and the first early-warning threshold is smaller than the second early-warning threshold, and the running state of the motor is divided into three sections by the first early-warning threshold and the second early-warning threshold;
the prediction analysis module calculates and acquires a state coefficient zt x After the value, if the state coefficient zt x If the value is larger than the second early warning threshold value, predicting that the motor of the vehicle is abnormal and not supporting the vehicle to continue running;
if the state coefficient zt x If the value is less than or equal to the first early warning threshold value, predicting that the motor of the vehicle is not abnormal and supporting the vehicleContinuing to run;
if the first early warning threshold value is smaller than the state coefficient zt x And if the value is less than or equal to the second early warning threshold value, predicting that the motor of the vehicle is slightly abnormal, but supporting the vehicle to continue running.
In a preferred embodiment, the intelligent reminding module intelligently calculates and reduces the highest running speed of the current vehicle, and the expression is:
wherein zg x Zg for correcting the running speed of the vehicle c Zt is the current running speed of the vehicle x Is a state coefficient.
In a preferred embodiment, the logic for acquiring the period of the current fluctuation warning is: the time period when the current fluctuation exceeds the current fluctuation threshold value is the time period of current fluctuation early warning;
the acquisition logic of the time period of the voltage fluctuation early warning is as follows: the time period when the voltage fluctuation exceeds the voltage fluctuation threshold value is the time period of the voltage fluctuation early warning.
In the technical scheme, the invention has the technical effects and advantages that:
the invention collects the electricity data and the mechanical data of the motor through the data collection module in the running process of the vehicle, the prediction analysis module is started when judging that the vehicle is located on the expressway, the state coefficient is generated for the motor after the electricity data and the mechanical data are calculated comprehensively, the running state of the motor is analyzed according to the comparison result of the state coefficient and the gradient early warning threshold value, the intelligent reminding module intelligently calculates and reduces the highest running speed of the current vehicle when the vehicle is predicted to support the vehicle to continue running and slightly abnormal exists, and sends the calculation result to the driver, and if the highest running speed of the current vehicle after calculation is lower than the lowest speed limit of the expressway, or the vehicle motor is predicted to not support the vehicle to continue running, a second warning signal is sent to the driver to remind the driver to drive the vehicle to enter an emergency lane. The system can predict the faults of the motor and reasonably prompt the faults when the electric automobile runs on the expressway, and the safety of the electric automobile is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a block diagram of a system according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, the information system for intelligent early warning and analysis of motor faults in this embodiment includes a driving road judging module, a first warning module, a data collecting module, a predictive analysis module, an intelligent reminding module and a second warning module;
and a driving road judging module: whether the vehicle is located on the expressway or not is judged based on shooting of a vehicle navigation system or a vehicle camera, and a judgment result is sent to a prediction analysis module and a first warning module, specifically:
and (3) data acquisition: the relevant data is acquired using a vehicle navigation system or a vehicle camera. The navigation system may provide the current location and speed of travel of the vehicle, while the camera may capture images or video of the road.
Geographic location data analysis: if a navigation system is used, the geographic location data of the vehicle is first analyzed. Expressways typically have specific geographical coordinates and road information, including entrance and exit positions.
And (3) analyzing camera data: if camera data is used, image processing and computer vision analysis are required. This may include the steps of:
lane detection: the lane in which the vehicle is located is identified.
Road sign detection: markers on the road, such as entrance and exit markers on the highway, are detected.
And (3) vehicle detection: the presence and direction of travel of other vehicles are detected.
Judging the position of the vehicle: and determining the current position of the vehicle, namely whether the vehicle is positioned on the expressway or not according to the geographic position data and the camera data. This can be achieved by comparing the vehicle location with the road information.
And sending a judging result: and sending the judgment result to the prediction analysis module and the first warning module. If it is determined that the vehicle is on a highway, a corresponding flag or indication will be sent.
The first warning module: the vehicle speed warning device is used for warning the running speed range of the vehicle, when the real-time running speed of the vehicle is not in the expressway running speed range, a first warning signal is sent to a driver to remind the driver to run the vehicle in the running speed range, and specifically comprises the following steps:
acquiring the real-time speed of the vehicle: the current real-time travel speed is obtained using the vehicle's sensors or vehicle data.
Acquiring a driving speed range of a highway: and acquiring an allowable speed range on the expressway. This is typically set by traffic authorities and varies depending on road conditions, traffic flow, weather, etc.
Comparing real-time speed to highway speed range: the real-time speed of the vehicle is compared to the allowable speed range of the highway.
Triggering a first warning signal: if the real-time speed of the vehicle is not in the driving speed range of the expressway, the first warning module sends a warning signal to the driver so as to remind the driver to adjust the speed of the vehicle. This can be achieved by:
displaying warning information: a warning message is displayed on the vehicle dashboard to inform the driver that a speed reduction or increase is required.
Sounding an alarm: an audible or warning signal is emitted to the driver.
Vibration seats or steering wheels: the driver is alerted by vibrating the seat or steering wheel.
The navigation system prompts: with a vehicle navigation system, a suggested travel speed is displayed or a driver is reminded to accelerate or decelerate.
Updating warning information in real time: continuously monitoring the real-time speed of the vehicle, and updating the warning information in real time according to the situation. If the driver adjusts the vehicle speed to match the highway speed range, the alert may be automatically cancelled.
And a data acquisition module: in the running process of the vehicle, electricity data and mechanical data of the motor are collected, preprocessed and sent to the prediction analysis module.
The prediction analysis module: when the vehicle is judged to be located on the expressway and driven (if the vehicle is located on the urban road and driven, the vehicle speed of the urban road is low, fault analysis is only carried out on the vehicle at the moment, operation load and energy consumption of a prediction analysis module are reduced), state coefficients are generated for the motor after electricity data and mechanical data are comprehensively calculated, the operation state of the motor is analyzed according to the comparison result of the state coefficients and gradient early warning thresholds, the prediction result is sent to a second warning module and an intelligent reminding module, and the state coefficients are sent to the intelligent reminding module.
And the intelligent reminding module is used for: when the predicted vehicle motor supports the vehicle to continue running and there is a slight abnormality, the highest running speed of the current vehicle is intelligently calculated to be reduced, and the calculation result is sent to the driver.
The second warning module: if the calculated highest running speed of the current vehicle is lower than the lowest speed limit of the expressway, or the predicted vehicle motor does not support the vehicle to continue running, a second warning signal is sent to the driver to remind the driver of needing to drive the vehicle into an emergency lane (if a service area exists in the 1 km forward direction of the vehicle, the driver can be reminded of entering the service area), specifically:
calculating the highest running speed: the highest running speed of the current vehicle is calculated using the sensors and data sources of the vehicle. This may be estimated based on factors such as vehicle performance parameters, battery status, road conditions, etc.
Detecting the lowest speed limit of the expressway: and acquiring the minimum speed limit information on the expressway. This is typically set by traffic authorities and may vary due to road conditions, weather, etc.
Detecting the state of the motor: the motor state of the electric automobile is monitored, including battery power, state of charge, motor temperature and the like. If the motor state does not support continued travel, an alert needs to be triggered.
Comparing the speed and the speed limit: the highest running speed of the current vehicle is compared with the lowest speed limit of the highway. If the current highest speed of the vehicle is below the lowest speed limit of the highway, a second alert needs to be triggered.
Predicting battery depletion: data and predictive models of the electric vehicle are used to estimate whether the battery will be depleted in the near future. If the battery is about to run out, a second alert needs to be triggered.
Sending a second warning signal: if any of the above conditions are met, the second alert module should send an alert signal to the driver to alert them to take appropriate action. This can be achieved by:
displaying warning information: a warning message is displayed on the vehicle dashboard to inform the driver that action needs to be taken.
Sounding an alarm: an audible or warning signal is emitted to the driver.
Vibration seats or steering wheels: the driver is alerted by vibrating the seat or steering wheel.
The navigation system prompts: the suggested travel route or the location of the service area is displayed using the vehicle navigation system.
Providing emergency lane and service area advice: if the vehicle is traveling in a direction of 1 km with a service area, the system may alert the driver to enter the service area to charge or rest. If there is no service area, the system should recommend the driver to enter the emergency lane of the highway to ensure safety.
Specifically, for the expressway, the usual driving speed range is 60-100 km/h or 60-120 km/h, however, when the expressway encounters heavy fog or rainy days, the speed is recommended to be reduced to 80km/h, and when the expressway encounters a construction road section, the highest speed is limited to be lower than 80km/h or 60km/h, so that the first warning module and the second warning module judge that the highest driving speed of the expressway can acquire the current limited maximum speed and the driving speed range according to the traffic speed limit plate prompted by the navigation system or identified by the vehicle camera.
According to the method, the data acquisition module is used for acquiring the electricity data and the mechanical data of the motor in the running process of the vehicle, the prediction analysis module is started when the vehicle is judged to be located on the expressway, the electricity data and the mechanical data are comprehensively calculated to generate a state coefficient for the motor, the running state of the motor is analyzed according to the comparison result of the state coefficient and the gradient early warning threshold value, the intelligent reminding module intelligently calculates and reduces the highest running speed of the current vehicle when the vehicle is predicted to support the vehicle to continue running and sends the calculation result to the driver when slight abnormality exists, and if the highest running speed of the current vehicle after calculation is lower than the lowest speed limit of the expressway or the vehicle is predicted to not support the vehicle to continue running, a second warning signal is sent to the driver to remind the driver of the need to drive the vehicle to enter an emergency lane. The system can predict the faults of the motor and reasonably prompt the faults when the electric automobile runs on the expressway, and the safety of the electric automobile is greatly improved.
Example 2: the data acquisition module acquires electricity data and mechanical data of the motor in the running process of the vehicle, and preprocesses the electricity data and the mechanical data comprises the following steps:
and (3) data storage: storing the collected data in a data storage device in the vehicle, such as a data log file, a memory or a solid state disk, wherein the data is recorded in a time sequence mode for subsequent analysis;
data filtering and noise reduction: filtering and noise reduction processing is carried out on mechanical data and electricity consumption data to remove possible noise and interference, so that the accuracy and reliability of the data are ensured, and the filtering method can comprise median filtering, mean filtering, gaussian filtering and the like;
data calibration: calibrating the sensor data to eliminate errors and drift of the sensor, wherein the calibration can be performed according to actual conditions to ensure the accuracy of the data;
data timestamp marking: adding a time stamp to each data point to record the time information of the data acquisition, which is important for subsequent time series analysis and event correlation;
data missing value processing: checking for missing values in the data (e.g., loss of data due to sensor failure) and taking appropriate action to fill in or process the missing values to avoid adversely affecting the analysis;
data format normalization: normalizing the data formats of the different sensors for unified analysis, which may include unit conversion, data normalization, or allocation of standard units;
data compression: when data is stored and transmitted, the data can be compressed to reduce the use of storage space and bandwidth, and a compression algorithm is selected according to the data type and requirements;
checking data quality: quality checks are performed on the processed data to ensure data integrity and consistency, including checking for outliers, trend anomalies, and the like.
The data acquisition module acquires electricity data and mechanical data of the motor in the running process of the vehicle, wherein the electricity data comprises a coil magnetic field floating coefficient, and the mechanical data comprises vibration amplitude, noise decibel and rotating speed fluctuation amplitude;
the calculation expression of the coil magnetic field floating coefficient is as follows:
wherein xc is the coil magnetic field floating coefficient, C (t) is the magnetic field intensity variation of the motor coil during the running process of the electric automobile, [ t ] x ，t y ]For the period of early warning of current fluctuation, [ t ] i ，t j ]A time period for early warning of voltage fluctuation;
the acquisition logic of the time period of the current fluctuation early warning is as follows: current ripple has a significant impact on motor coil field strength because current is the primary driving factor in generating motor magnetic fields, motor performance and operating characteristics are directly dependent on field strength and control, and the period in which current ripple exceeds the current ripple threshold is the period in which current ripple is pre-warned.
The main effect of current ripple on motor coil field strength is as follows:
the change of the current can directly lead to the change of the magnetic field intensity in the motor coil, the increase of the current can strengthen the intensity of the magnetic field, the decrease of the current can weaken the magnetic field, which is of great importance to the torque and the performance of the motor, because the intensity of the magnetic field determines the force and the torque generated by the motor, the direction of the current determines the direction of the magnetic field in the coil, the magnetic field can rotate clockwise or anticlockwise according to the direction of the current, which is very important to the rotation direction and the steering control of the motor, the change of the current and the voltage can lead to the change of the electromagnetic induction in the motor, which generates back electromotive force, the mechanical movement of the motor is caused, and the current fluctuation can influence the magnitude of the electromagnetic induction, thereby influencing the running speed and the output power of the motor.
The acquisition logic of the time period of the voltage fluctuation early warning is as follows: the voltage fluctuation can directly influence the magnetic field intensity of the motor coil, because the voltage is a driving force for generating current, the current is one of main factors for generating the magnetic field, the performance and the operation characteristics of the motor are influenced by the magnetic field intensity, and the time period when the voltage fluctuation exceeds the voltage fluctuation threshold value is the time period of voltage fluctuation early warning;
voltage fluctuations may have the following effects on the motor:
the change in voltage will cause a change in current and thus affect the magnetic field strength in the motor coil, a higher voltage will typically result in a larger current and thus increase the strength of the magnetic field, a lower voltage will decrease the current and result in a weakening of the magnetic field, a change in voltage will also affect the direction of the current and thus change the direction of the magnetic field in the coil, which is important for the rotational direction and steering control of the motor, a change in magnetic field strength will directly affect the performance of the motor, including the torque, output power and operating speed produced, a higher magnetic field strength will typically increase the performance of the motor, while a lower magnetic field strength may decrease the performance, a voltage fluctuation will result in a change in current and thus affect the electromagnetic induction in the motor, which will affect the output and operating speed of the motor, and frequent voltage fluctuations may result in oscillations or instability of the motor, which may affect the reliability and stability of the motor.
The acquisition logic of the vibration amplitude is as follows:
the vibration sensor or the acceleration sensor is installed by a suitable position around the motor or the motor. These sensors may measure vibrations generated by the motor and convert them into electrical signals, connecting the output signal lines of the sensor to a data acquisition system or control unit, which typically generates analog voltage signals, and thus may require an analog signal processor or analog-to-digital converter (ADC) to convert the signals into digital form for processing, the data acquisition system typically being part of a vehicle control system for receiving, storing and processing the sensor data. These systems may be part of a dedicated vibration monitoring system or an overall control system for the vehicle to obtain the vibration amplitude of the motor in real time during the travel of the vehicle;
the larger the vibration amplitude, the following problems may be caused:
mechanical damage: the strong vibrations may cause mechanical damage to the motor and its accessory components (e.g., bearings, seals, etc.), which may shorten the life of the motor, increasing the cost of maintenance and replacement of the components;
performance degradation: vibrations may affect motor performance, including torque output and rotational speed, which may lead to reduced vehicle dynamics, such as acceleration and peak speed;
noise problem: the intense vibrations are often accompanied by noise problems, the noise level in the vehicle can rise, reducing the comfort of the driver and passengers, and also affecting the acoustic performance of the vehicle;
battery damage: vibrations may also adversely affect the battery pack, resulting in damage or reduced life of the battery pack, and the stability and safety of the battery pack may be compromised;
energy efficiency decreases: vibration increases loss of mechanical energy, resulting in a decrease in energy efficiency, which means that an electric vehicle may require more electric energy to accomplish the same task, resulting in a decrease in endurance mileage;
driving comfort decreases: the strong vibrations can affect the comfort of the driver and passengers and can lead to fatigue and discomfort, especially during long distance driving;
system failure: vibrations may cause other system failures of the electric vehicle, such as suspension systems, steering systems, and braking systems, which may increase unreliability and maintenance costs of the vehicle.
The logic for obtaining the noise decibels is as follows:
mounting a sound sensor at a suitable location inside or outside the electric vehicle, the sensor should be able to effectively capture noise generated by the motor, connecting the output signal line of the sensor to a data acquisition system or control unit, the sensor typically generating an analog voltage signal, and thus the sensor may require an analog signal processor or analog-to-digital converter (ADC) to convert the signal to digital form for processing, the data acquisition system typically being part of a vehicle control system for receiving, storing and processing the sound data of the sensor, which may be part of a dedicated sound monitoring system or the overall control system of the vehicle, through which the output signal of the sound sensor will be captured and recorded in real time, the sound data being stored in digital form, acquiring the real time noise decibels of the motor;
the greater the noise decibels, the following problems can result:
intense motor noise may be an indication of a mechanical failure or problem with a mechanical component, for example, bearings, gears, drive trains, or other mechanical components may have worn or damaged, resulting in increased noise levels, motor imbalance may result in increased vibration and noise, motor imbalance may be due to problems in the manufacturing process or component wear, abnormal current or voltage waveforms may result in increased motor noise, problems with the motor electrical system may require inspection and repair, problems with the motor cooling system such as insufficient coolant circulation or cooling fans may result in overheating of the motor, additional noise is generated, insufficient maintenance of the motor may result in component wear or accumulation of dirt, thereby increasing noise levels, periodic maintenance and servicing is important to ensure proper operation of the motor and reduce noise.
The calculation expression of the rotation speed fluctuation amplitude is as follows:
wherein zb is the amplitude of rotation speed fluctuation, Z Real world y Z is the actual rotation speed of the motor at the y-th sampling point in the T time Flat plate avg For the average rotation speed of the motor in the time T, y=1, 2, 3, 4, … …, m is the number of sampling points of the actual rotation speed;
the larger the rotational speed fluctuation amplitude is, the following problems may be shown:
mechanical failure: mechanical components inside the motor, such as bearings, gears or transmission systems, may present problems that may lead to instability and vibration of the motor, which in turn may cause rotational speed fluctuations;
electrical problems: abnormal current or voltage fluctuations may cause unstable rotational speeds of the motor, which may be problematic in the electrical system of the motor, requiring inspection and repair;
software problem: the motor of the electric automobile is generally managed by control software, and software problems or errors of a control algorithm can cause instability and rotation speed fluctuation of the motor;
sensor problem: sensors for motor performance monitoring may be problematic, for example, failure of a rotational speed sensor or an angle sensor may result in inaccurate rotational speed measurements, thereby causing fluctuations;
cooling problem: cooling system problems of the motor, such as insufficient coolant flow or overheating problems, may cause performance degradation and rotational speed fluctuation of the motor;
the maintenance is insufficient: insufficient maintenance of the motor may cause wear of mechanical parts or accumulation of dirt, which may cause fluctuations in rotational speed;
load problem: the load of the motor may be unbalanced or abnormal, resulting in speed fluctuations, which may be due to problems with the drive train or other mechanical components anomalies;
battery problem: the battery pack of the electric vehicle may have problems such as insufficient battery capacity or voltage fluctuation, which may affect the performance and stability of the motor.
The prediction analysis module is started when judging that the vehicle is located on the expressway (if the vehicle is located on the urban road and the speed of the vehicle is low, the vehicle is only subjected to fault analysis at the moment, the operation load and the energy consumption of the prediction analysis module are reduced), the power consumption data and the mechanical data are comprehensively calculated to generate a state coefficient for the motor, and the operation state of the motor is analyzed according to the comparison result of the state coefficient and the gradient early warning threshold;
the predictive analysis module comprehensively calculates the coil magnetic field floating coefficient, vibration amplitude, noise decibel and rotational speed fluctuation amplitude to obtain a state coefficient zt x The expression is:
wherein zd is vibration amplitude, zf is noise decibel, xc is coil magnetic field floating coefficient, zb is rotation speed fluctuation amplitude, alpha, beta, gamma and omega are respectively vibration amplitude, noise decibel, coil magnetic field floating coefficient and rotation speed fluctuation amplitude proportionality coefficient, and alpha, beta, gamma and omega are all more than 0;
from the acquisition formula or logic of each data and the calculation expression of the state coefficient, the state coefficient zt is known x The smaller the motor, the more stable the motor running state, the less likely to fail, the state coefficient zt x The larger the motor, the more unstable the running state of the motor, and the more likely to be in fault;
the gradient early warning threshold comprises a first early warning threshold and a second early warning threshold, the first early warning threshold is smaller than the second early warning threshold, and the running state of the motor is divided into three sections through the first early warning threshold and the second early warning threshold;
the prediction analysis module calculates and acquires a state coefficient zt x After the value, if the state coefficient zt x If the value is larger than the second early warning threshold value, predicting that the motor of the vehicle is abnormal and not supporting the vehicle to continue running;
if the state coefficient zt x If the value is less than or equal to the first early warning threshold value, predicting that the motor of the vehicle is not abnormal and supporting the vehicle to continue running;
if the first early warning threshold value is smaller than the state coefficient zt x If the value is less than or equal to the second early warning threshold value, predicting that a slight abnormality occurs in the motor of the vehicle, but supporting the vehicle to continue running;
when the predicted vehicle motor supports the vehicle to continue running and slight abnormality exists, the intelligent reminding module intelligently calculates and reduces the highest running speed of the current vehicle and sends a calculation result to a driver;
the intelligent reminding module intelligently calculates and reduces the highest running speed of the current vehicle, and the expression is as follows:
wherein zg x Zg for correcting the running speed of the vehicle c Zt is the current running speed of the vehicle x Is a state coefficient;
after the intelligent reminding module obtains the corrected running speed of the vehicle, the intelligent reminding module prompts the driver, and when the corrected running speed of the vehicle is lower than the lowest speed limit of the expressway, the second warning module prompts the driver to enter an emergency lane to stop running.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. An information system for intelligent early warning and analysis of motor faults is characterized in that: the system comprises a driving road judging module, a first warning module, a data acquisition module, a prediction analysis module, an intelligent reminding module and a second warning module;
and a driving road judging module: judging whether the vehicle is on the expressway or not based on the shooting of a vehicle navigation system or a vehicle camera;
the first warning module: the vehicle speed warning device is used for warning the running speed range of the vehicle;
and a data acquisition module: during the running process of the vehicle, collecting the electricity data and the mechanical data of the motor, and preprocessing the electricity data and the mechanical data;
the prediction analysis module: starting when the vehicle is judged to be positioned on a highway and running, generating a state coefficient for the motor after comprehensively calculating electricity data and mechanical data, and analyzing the running state of the motor according to the comparison result of the state coefficient and the gradient early warning threshold value;
and the intelligent reminding module is used for: when the predicted vehicle motor supports the vehicle to continue running and slight abnormality exists, intelligently calculating to reduce the highest running speed of the current vehicle, and sending a calculation result to a driver;
the second warning module: and if the calculated highest running speed of the current vehicle is lower than the lowest speed limit of the expressway, or the motor of the vehicle is predicted to not support the vehicle to continue running, sending a second warning signal to the driver to remind the driver of needing to drive the vehicle to enter an emergency lane.
2. The information system for intelligent early warning and analysis of motor faults according to claim 1, wherein: the data acquisition module acquires electricity data and mechanical data of the motor in the running process of the vehicle, wherein the electricity data comprise coil magnetic field floating coefficients, and the mechanical data comprise vibration amplitude, noise decibels and rotating speed fluctuation amplitude.
3. The information system for intelligent early warning and analysis of motor faults according to claim 2, wherein: the predictive analysis module comprehensively calculates the coil magnetic field floating coefficient, vibration amplitude, noise decibel and rotational speed fluctuation amplitude to obtain a state coefficient zt x The expression is:
wherein zd is vibration amplitude, zf is noise decibel, xc is coil magnetic field floating coefficient, zb is rotation speed fluctuation amplitude, alpha, beta, gamma and omega are respectively vibration amplitude, noise decibel, coil magnetic field floating coefficient and rotation speed fluctuation amplitude proportionality coefficient, and alpha, beta, gamma and omega are all more than 0.
4. An intelligent motor fault warning and analysis information system according to claim 3, wherein: the calculation expression of the coil magnetic field floating coefficient is as follows:
wherein xc is the coil magnetic field floating coefficient, C (t) is the magnetic field intensity variation of the motor coil during the running process of the electric automobile, [ t ] x ，t y ]For the period of early warning of current fluctuation, [ t ] i ，t j ]And the time period of early warning for voltage fluctuation.
5. The information system for intelligent early warning and analysis of motor faults according to claim 4, wherein: the calculation expression of the rotation speed fluctuation amplitude is as follows:
wherein zb is the amplitude of rotation speed fluctuation, Z Real world y Z is the actual rotation speed of the motor at the y-th sampling point in the T time Flat plate avg For the average rotational speed of the motor in time T, y=1, 2, 3, 4, … …, m is the number of sampling points of the actual rotational speed.
6. The information system for intelligent early warning and analysis of motor faults according to claim 5, wherein: the gradient early warning threshold comprises a first early warning threshold and a second early warning threshold, the first early warning threshold is smaller than the second early warning threshold, and the running state of the motor is divided into three sections through the first early warning threshold and the second early warning threshold;
the prediction analysis module calculates and acquires a state coefficient zt x After the value, if the state coefficient zt x If the value is larger than the second early warning threshold value, predicting that the motor of the vehicle is abnormal and not supporting the vehicle to continue running;
if the state coefficient zt x If the value is less than or equal to the first early warning threshold value, predicting that the motor of the vehicle is not abnormal and supporting the vehicle to continue running;
if the first early warning threshold value is smaller than the state coefficient zt x And if the value is less than or equal to the second early warning threshold value, predicting that the motor of the vehicle is slightly abnormal, but supporting the vehicle to continue running.
7. The information system for intelligent motor fault pre-warning and analysis according to claim 6, wherein: the intelligent reminding module intelligently calculates and reduces the highest running speed of the current vehicle, and the expression is as follows:
wherein zg x Zg for correcting the running speed of the vehicle c Zt is the current running speed of the vehicle x Is a state coefficient.
8. The information system for intelligent motor fault pre-warning and analysis according to claim 7, wherein: the acquisition logic of the time period of the current fluctuation early warning is as follows: the time period when the current fluctuation exceeds the current fluctuation threshold value is the time period of current fluctuation early warning;
the acquisition logic of the time period of the voltage fluctuation early warning is as follows: the time period when the voltage fluctuation exceeds the voltage fluctuation threshold value is the time period of the voltage fluctuation early warning.
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