A wind turbine generator bearing high temperature fault diagnosis system

Abstract

The present invention discloses a high-temperature fault diagnosis system for a bearing of a wind turbine generator, and relates to the technical field of wind turbine fault diagnosis. At present, most systems of high-temperature fault diagnosis systems for bearings of wind turbine generators independently detect a single unit, and are unable to consider the mutual influence and correlation between units, making it difficult to fully grasp the overall operating status of the wind farm, and unable to discover the performance degradation trend and potential hidden dangers of the bearings in advance, making it difficult to perform preventive maintenance, thereby increasing the risk of sudden failures of the wind turbine generators. The present invention associates wind turbines in a group, performs temperature comparison and analysis according to height information groups, and achieves accurate diagnosis and timely processing of high-temperature faults of bearings of wind turbine generators, thereby extending the life of bearings, reducing maintenance costs, providing accurate data support for wind farm operation management, enhancing the reliability and stability of wind farms, reducing downtime due to failures, and increasing power generation.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbine fault diagnosis, and in particular to a wind turbine generator bearing high temperature fault diagnosis system. Background Art

[0002] The high temperature fault diagnosis system of wind turbine generator bearing is an important technical means for monitoring and diagnosing abnormal temperature of wind turbine generator bearing. The system usually consists of multiple parts, including sensors, data acquisition equipment, communication network, central processing unit, monitoring and alarm devices, etc.

[0003] In this regard, the patent document with publication number CN112651426A discloses a method for diagnosing rolling bearing faults of wind turbines, which effectively solves the gradient vanishing problem of traditional RNNs. Compared with long short-term memory neural networks in a general sense, the GRU model is simpler, has a faster training speed, and is suitable for processing larger-scale data. Based on this, the status of wind turbine bearings is diagnosed.

[0004] At present, most wind turbine bearing high temperature fault diagnosis systems detect individual units independently, and cannot consider the mutual influence and correlation between units, making it difficult to fully grasp the overall operating status of the wind farm. In addition, it is difficult to detect the propagation trend in time when multiple units fail, which affects the power generation efficiency. On the other hand, temperature detection is easily affected by environmental factors, making it difficult to accurately extract fault characteristics to distinguish different types, and prone to misjudgment and omission. It is impossible to detect the decline trend of bearing performance and potential hidden dangers in advance, making it difficult to perform preventive maintenance, which increases the risk of sudden failure of wind turbines.

[0005] In view of the above problems, a high temperature fault diagnosis system for generator bearings of wind turbines is proposed. Summary of the invention

[0006] The object of the present invention is to provide a wind turbine generator bearing high temperature fault diagnosis system, which solves the problem that the diagnosis system in the background technology is difficult to perform preventive diagnosis.

[0007] To achieve the above object, the present invention provides the following technical solution: a wind turbine generator bearing high temperature fault diagnosis system, comprising:

[0008] Communication configuration:

[0009] Establish the fan unit communication network, perform network settings and connection tests, and configure the communication protocol;

[0010] Create a database table structure, including wind turbine number, collection time, temperature value, height information, wind turbine status, establish a cable, set the data collection frequency, set it according to historical data, and determine the temperature anomaly threshold range;

[0011] Data acquisition cycle:

[0012] Send data collection instructions to each wind turbine in a preset order, wait for the response of the wind turbine using the communication protocol, receive the temperature data, height information and other related parameters returned by the wind turbine, check the integrity of the data, perform rationality verification, and store the verified data in the database;

[0013] Temperature comparison analysis:

[0014] Read the temperature data and height information of all wind turbines at the current time point from the database, group the wind turbines according to the height information, set the height interval, with 10 meters as a unit interval, and determine the temperature anomaly threshold range based on the operating environment of the wind turbines and relevant technical standards and empirical data;

[0015] According to the height information of the wind turbines, the wind turbines in a similar height range are grouped. For each height group, the average temperature of the wind turbines in the group is calculated, and the standard deviation of the temperature is calculated to determine the degree of temperature dispersion. The temperature of each wind turbine in the group is checked one by one to determine whether the difference between the temperature and the average temperature exceeds the preset abnormal threshold range, and the wind turbines with abnormal temperatures are marked.

[0016] For each altitude grouping, calculate the average temperature:

[0017]

[0018] Where T i is the temperature of the i-th wind turbine unit in the group, and n is the number of wind turbine units in the group;

[0019] Calculate the standard deviation of temperature:

[0020]

[0021] Abnormal judgment, check the temperature T of each wind turbine in the group one by one i , if T i <T avg -kσ or T i >T avg +kσ, the wind turbine is marked as having abnormal temperature;

[0022] For wind turbines with abnormal temperatures, the proportional adjustment method is used to calculate the appropriate speed adjustment value, and the speed is reduced in proportion to the temperature deviation. The calculation formula is as follows:

[0023] The speed is R and the temperature deviation is:

[0024] ΔT=|T i -Tavg |

[0025] The adjusted speed calculation formula is:

[0026]

[0027] For wind turbines marked as having abnormal temperature, a speed adjustment instruction is sent to the wind turbine to reduce its speed. The instruction contains the speed adjustment value, and the abnormal information is recorded in the log. The temperature value, height information and status of each wind turbine are displayed, the data is updated in real time, and an alarm prompt is sent to the monitoring interface.

[0028] Preferably, an industrial Ethernet switch is used to connect the wind turbines to form a local area network, an Ethernet interface module is installed on each wind turbine to communicate with the central control system, a unique IP address is assigned to each wind turbine and the central control system using a static IP address, and a subnet mask and a gateway are set: according to the network planning, a suitable subnet mask and a gateway address are set;

[0029] Parameter settings:

[0030] Analyze the historical temperature data of wind turbines, calculate the average temperature and standard deviation, and set the temperature anomaly threshold range to the average temperature plus or minus a certain multiple of the standard deviation based on statistical principles. The calculation formula is as follows:

[0031] Average temperature T avg , standard deviation is σ, temperature anomaly threshold range is T avg ±kσ, where k is a constant and the k value range is [2, 3]. Under normal circumstances, the temperature value falls within ±2 or 3 times the standard deviation of the average temperature.

[0032] Preferably, the data sending and collection instructions are sent in sequence according to the wind turbine unit number, and the data collection instructions are sent to each wind turbine unit using the configured communication protocol. The instructions include the information that needs to be collected, such as temperature, height, and collection time interval. While sending the instructions, the time of sending the instructions is recorded.

[0033] Preferably, the length of the received data is checked, and if the data length does not match the preset data format, it is located as a transmission error. For the temperature data and the height information, it is checked whether there are missing values.

[0034] Preferably, for a wind turbine set marked as having abnormal temperature, its temperature deviation is calculated. The temperature deviation is the difference between the current temperature and the average temperature of the altitude group, and the formula is:

[0035]

[0036] Where T 异常 For the abnormal fan unit, is the average grouping of this height grouping.

[0037] Preferably, a proportional adjustment method is used to determine the speed adjustment value according to the size ratio of the temperature deviation, and the proportional coefficient is p, then:

[0038] Preferably, an instruction including a rotation speed adjustment value is generated, and the instruction is sent to the wind turbine set with abnormal temperature using a communication protocol to ensure that the instruction can be correctly received and executed by the wind turbine set.

[0039] Preferably, the number or identifier of the wind turbine set is added to the instruction, and the serial number of the instruction is added for confirmation and tracking of the instruction.

[0040] Preferably, a checksum field is added to the instruction to detect whether an error occurs during the transmission of the instruction. The checksum uses a CRC-16 algorithm to calculate a 16-bit checksum and adds it to the end of the instruction, adding some redundant information to the instruction.

[0041] Preferably, when the wind turbine generator system detects a checksum error or redundant information inconsistency in an instruction, the wind turbine generator system resends the instruction and takes other error handling measures according to the error message.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention provides a wind turbine generator bearing high temperature fault diagnosis system, which associates wind turbines in a group, performs temperature comparison analysis according to height information groups, and realizes accurate diagnosis and timely processing of wind turbine generator bearing high temperature faults. Specifically, the system reads the temperature and height information of the wind turbines from the database and groups them, calculates the average temperature and standard deviation of each group to determine the abnormal threshold range, marks the units with abnormal temperatures and calculates the temperature deviation, uses the proportional adjustment method to determine the speed adjustment value and then sends the instruction. This solution improves the accuracy of temperature detection, reduces the fault misjudgment and missed judgment rate, can promptly detect units with abnormal temperatures and adjust the speed to reduce metal fatigue, extend bearing life, reduce maintenance costs, provide accurate data support for wind farm operation management, enhance the reliability and stability of wind farms, reduce downtime due to failures, and increase power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a process framework diagram of the present invention;

[0045] Figure 2 This is the decision logic diagram of the diagnostic system of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings.

[0048] Combination Figure 1-Figure 2 A wind turbine generator bearing high temperature fault diagnosis system of the present invention comprises:

[0049] Communication configuration:

[0050] Establish the fan unit communication network, perform network settings and connection tests, and configure the communication protocol;

[0051] Create a database table structure, including wind turbine number, collection time, temperature value, height information, wind turbine status, establish a cable, set the data collection frequency, set it according to historical data, and determine the temperature anomaly threshold range;

[0052] The data sending and collection instructions are sent in sequence according to the wind turbine unit number. The data collection instructions are sent to each wind turbine unit using the configured communication protocol. The instructions include the information that needs to be collected, such as temperature, height, and the collection time interval. While sending the instructions, the time of sending the instructions is recorded, and the length of the received data is checked. If the data length does not match the preset data format, it is located as a transmission error. For temperature data and height information, check whether there are missing values.

[0053] Data acquisition cycle:

[0054] Send data collection instructions to each wind turbine in a preset order, wait for the response of the wind turbine using the communication protocol, receive the temperature data, height information and other related parameters returned by the wind turbine, check the integrity of the data, perform rationality verification, and store the verified data in the database;

[0055] Use industrial Ethernet switches to connect wind turbines to form a local area network. Install Ethernet interface modules on each wind turbine to communicate with the central control system. Use static IP addresses to assign unique IP addresses to each wind turbine and central control system. Set subnet masks and gateways: Set appropriate subnet masks and gateway addresses according to network planning.

[0056] Parameter settings:

[0057] Analyze the historical temperature data of wind turbines, calculate the average temperature and standard deviation, and set the temperature anomaly threshold range to the average temperature plus or minus a certain multiple of the standard deviation based on statistical principles. The calculation formula is as follows:

[0058] Average temperature T avg , the standard deviation is σ, and the temperature anomaly threshold range is T avg ±kσ, where k is a constant and the k value range is [2, 3]. Under normal circumstances, the temperature value falls within ±2 or 3 times the standard deviation of the average temperature;

[0059] Temperature comparison analysis:

[0060] Read the temperature data and height information of all wind turbines at the current time point from the database, group the wind turbines according to the height information, set the height interval, with 10 meters as a unit interval, and determine the temperature anomaly threshold range based on the operating environment of the wind turbines and relevant technical standards and empirical data;

[0061] For wind turbines marked as temperature anomalies, calculate their temperature deviation. The temperature deviation is the difference between the current temperature and the average temperature of the altitude group. The formula is:

[0062]

[0063] Where T 异常 For the abnormal fan unit, is the average grouping of this height grouping;

[0064] The proportional adjustment method is used to determine the speed adjustment value according to the size ratio of the temperature deviation. The proportional coefficient is p, then:

[0065] Generate an instruction containing a speed adjustment value, and use a communication protocol to send the instruction to the wind turbine with abnormal temperature to ensure that the instruction can be correctly received and executed by the wind turbine.

[0066] According to the height information of the wind turbines, the wind turbines in a similar height range are grouped. For each height group, the average temperature of the wind turbines in the group is calculated, and the standard deviation of the temperature is calculated to determine the degree of temperature dispersion. The temperature of each wind turbine in the group is checked one by one to determine whether the difference between the temperature and the average temperature exceeds the preset abnormal threshold range, and the wind turbines with abnormal temperatures are marked.

[0067] For each altitude grouping, calculate the average temperature:

[0068]

[0069] Where T iis the temperature of the i-th wind turbine unit in the group, and n is the number of wind turbine units in the group;

[0070] Calculate the standard deviation of temperature:

[0071]

[0072] Abnormal judgment, check the temperature T of each wind turbine in the group one by one i , if T i <T avg -kσ or T i >T avg +kσ, the wind turbine is marked as having abnormal temperature;

[0073] By grouping wind turbines according to their height information and calculating the average temperature and standard deviation for each height group, the impact of differences in the environment of wind turbines at different heights on temperature can be more accurately considered. Compared with the traditional single temperature threshold detection method, this method can reduce temperature detection errors caused by environmental factors and improve the accuracy of temperature detection;

[0074] Traditional fault diagnosis systems often have difficulty accurately distinguishing different types of faults, and are prone to misjudgment or missed judgments. This method can more accurately diagnose high temperature bearing faults by performing detailed temperature analysis on each height group and setting the abnormal threshold range based on statistical principles. For wind turbines within a similar height range, their operating environment and temperature characteristics are more similar, so it is possible to more accurately determine which wind turbine temperature anomalies are real faults, reducing the probability of misjudgment and missed judgments.

[0075] For wind turbines with abnormal temperatures, the proportional adjustment method is used to calculate the appropriate speed adjustment value, and the speed is reduced in proportion to the temperature deviation. The calculation formula is as follows:

[0076] The speed is R and the temperature deviation is:

[0077] ΔT=|T i -T avg |

[0078] The adjusted speed calculation formula is:

[0079]

[0080] For a wind turbine set marked as having abnormal temperature, a speed adjustment instruction is sent to the wind turbine set to reduce its speed, wherein the instruction includes a speed adjustment value;

[0081] According to the size of the temperature deviation, it is divided into different levels, which are set to mild deviation, moderate deviation and severe deviation. Mild deviation means that the temperature deviation is within the range of plus or minus 1 standard deviation of the average temperature; moderate deviation means that the temperature deviation is within the range of plus or minus 1 to 2 standard deviations of the average temperature; severe deviation means that the temperature deviation exceeds the range of plus or minus 2 standard deviations of the average temperature.

[0082] Each temperature deviation level sets a corresponding speed adjustment ratio. The greater the temperature deviation, the higher the speed adjustment ratio. The speed adjustment ratio for mild deviation is 10%, for moderate deviation is 20%, and for severe deviation is 30%. The level to which it belongs is determined according to the temperature deviation, and the speed adjustment ratio corresponding to the level is adjusted. After adjusting the speed, the system continuously monitors the temperature change of the wind turbine. If the temperature gradually drops within a certain period of time and returns to the normal range, it means that the speed adjustment has taken effect. If the temperature continues to rise or has no obvious change, the system can further increase the speed adjustment ratio according to the situation, or take other measures, such as issuing an alarm to notify maintenance personnel to conduct on-site inspection and maintenance. Maintenance personnel can conduct a comprehensive inspection of the wind turbine according to the abnormal information and monitoring data provided by the system, determine the cause of the temperature abnormality, and take corresponding maintenance measures to ensure the safe and stable operation of the wind turbine.

[0083] Record abnormal information in the log, display the temperature value, height information and status of each wind turbine, update data in real time, and send alarm prompts to the monitoring interface;

[0084] Determine the speed adjustment strategy:

[0085] For example: The normal temperature range is T min to T max , when the exception is T 异常 ;

[0086] Temperature exceeds the range value ΔT=T 异常 -T max (When the temperature is lower than the normal range, ΔT = T min -T 异常 );

[0087] The maximum allowable speed adjustment ratio is P max ;

[0088] The speed adjustment ratio

[0089] Add the wind turbine group number or identifier to the instruction, add the instruction serial number for instruction confirmation and tracking, add a checksum field to the instruction to detect whether an error occurs during the transmission of the instruction, and use the CRC-16 algorithm to calculate a 16-bit checksum and add it to the end of the instruction. Add some redundant information to the instruction. When the wind turbine group detects a checksum error or inconsistent redundant information in the instruction, it resends the instruction and takes other error handling measures based on this error message.

[0090] When constructing the instruction format, the meaning and length of different fields are clearly divided. The number or identifier of the wind turbine is used to ensure that the instruction is accurately sent to the target unit; the serial number of the instruction can be used to track and sort the instructions so that the response can be correctly identified and processed when multiple instructions are sent at the same time. When the CRC-16 algorithm is used to calculate the checksum, all the data in the instruction are bitwise operated to obtain a 16-bit check code. Redundant information can include some repeated data fields or specific flag bits, which can be used to recover or verify the integrity of the data through redundant information when some data is damaged during transmission. Ethernet is selected to ensure that the instruction can be sent to the target wind turbine quickly and accurately. During the sending process, the communication status is monitored to ensure the success rate of the instruction. If the sending fails, you can try to resend or use an alternative communication channel. If the wind turbine detects a checksum error or inconsistent redundant information in the instruction, resend the instruction and take other error handling measures based on this error message. At the same time, the abnormal information is recorded in the log, showing the temperature value, height information and status of each wind turbine, updating the data in real time, and sending an alarm prompt to the monitoring interface. When the wind turbine feedback checksum error or redundant information is inconsistent, the system first analyzes the type and severity of the error. If it is a minor error, you can try to resend the command and check and correct the command before resending. If the error is more serious, the system can take other error handling measures, such as suspending the command sending to the wind turbine, troubleshooting and repairing. At the same time, the abnormal information is recorded in detail in the log, including the wind turbine number, abnormal temperature value, speed adjustment, and error type. The temperature value, height information and status of each wind turbine are updated in real time on the monitoring interface, and different colors or icons are used to indicate normal and abnormal states. When an abnormality occurs, an alarm prompt is sent to the monitoring interface to ensure that the staff can find and deal with the problem in time.

[0091] It can detect abnormal temperature wind turbines in time, and adjust the speed to reduce metal fatigue and extend the service life of bearings. This helps to reduce the maintenance cost of wind turbines and improve the overall operating efficiency of wind farms. At the same time, the system monitors and analyzes the temperature of wind turbines in real time, which can provide more accurate data support for the operation and management of wind farms and help managers make more scientific decisions. By associating wind turbines and using a system for temperature monitoring and fault diagnosis, the operating status of the entire wind farm can be fully grasped. When a wind turbine has an abnormal temperature, the system can take timely measures to avoid the propagation and spread of the fault, thereby improving the reliability and stability of the wind farm. In addition, the system's ability to accurately diagnose and handle faults in a timely manner can reduce the downtime of wind turbines due to faults and increase the power generation of wind farms.

[0092] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0093] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wind turbine generator bearing high temperature fault diagnosis system, characterized in that: include: Communication configuration: Establish the fan unit communication network, perform network settings and connection tests, and configure the communication protocol; Create a database table structure, including wind turbine number, collection time, temperature value, height information, wind turbine status, establish a cable, set the data collection frequency, set it according to historical data, and determine the temperature anomaly threshold range; Data collection loop: Send data collection instructions to each wind turbine in a preset order, wait for the response of the wind turbine using the communication protocol, receive the temperature data, height information and other related parameters returned by the wind turbine, check the integrity of the data, perform rationality verification, and store the verified data in the database; Temperature comparison analysis: Read the temperature data and height information of all wind turbines at the current time point from the database, group the wind turbines according to the height information, set the height interval, with 10 meters as a unit interval, and determine the temperature anomaly threshold range based on the operating environment of the wind turbines and relevant technical standards and empirical data; According to the height information of the wind turbines, the wind turbines in a similar height range are grouped. For each height group, the average temperature of the wind turbines in the group is calculated, and the standard deviation of the temperature is calculated to determine the degree of temperature dispersion. The temperature of each wind turbine in the group is checked one by one to determine whether the difference between the temperature and the average temperature exceeds the preset abnormal threshold range, and the wind turbines with abnormal temperatures are marked. For each altitude grouping, calculate the average temperature: Where T i is the temperature of the i-th wind turbine unit in the group, and n is the number of wind turbine units in the group; Calculate the standard deviation of temperature: Abnormal judgment, check the temperature T of each wind turbine in the group one by one i , if T i <T avg -kσ or T i >T avg +kσ, the wind turbine is marked as having abnormal temperature; For wind turbines with abnormal temperatures, the proportional adjustment method is used to calculate the appropriate speed adjustment value, and the speed is reduced in proportion to the temperature deviation. The calculation formula is as follows: The speed is R and the temperature deviation is: ΔT=|T i -T avg | The adjusted speed calculation formula is: The proportional adjustment method is used to determine the speed adjustment value according to the size ratio of the temperature deviation. The proportional coefficient is p, then: For wind turbines marked as having abnormal temperature, a speed adjustment instruction is sent to the wind turbine to reduce its speed. The instruction contains the speed adjustment value, and the abnormal information is recorded in the log. The temperature value, height information and status of each wind turbine are displayed, the data is updated in real time, and an alarm prompt is sent to the monitoring interface.

2. A wind turbine generator bearing high temperature fault diagnosis system according to claim 1, characterized in that: Use industrial Ethernet switches to connect wind turbines to form a local area network. Install Ethernet interface modules on each wind turbine to communicate with the central control system. Use static IP addresses to assign unique IP addresses to each wind turbine and central control system. Set subnet masks and gateways: Set appropriate subnet masks and gateway addresses according to network planning. Parameter settings: Analyze the historical temperature data of wind turbines, calculate the average temperature and standard deviation, and set the temperature anomaly threshold range to the average temperature plus or minus a certain multiple of the standard deviation based on statistical principles. The calculation formula is as follows: Average temperature T avg , the standard deviation is σ, and the temperature anomaly threshold range is T avg ±kσ, where k is a constant and the k value range is [2, 3]. Under normal circumstances, the temperature value falls within ±2 or 3 times the standard deviation of the average temperature.

3. A wind turbine generator bearing high temperature fault diagnosis system according to claim 1, characterized in that: The data collection instructions are sent in sequence according to the wind turbine unit number. The configured communication protocol is used to send data collection instructions to each wind turbine unit. The instructions include the information that needs to be collected, such as temperature, height, and collection time interval. While sending the instructions, the time of sending the instructions is recorded.

4. A wind turbine generator bearing high temperature fault diagnosis system according to claim 1, characterized in that: Check the length of the received data. If the data length does not match the preset data format, it is located as a transmission error. For temperature data and height information, check whether there are missing values.

5. A wind turbine generator bearing high temperature fault diagnosis system according to claim 1, characterized in that: For wind turbines marked as temperature anomalies, calculate their temperature deviation. The temperature deviation is the difference between the current temperature and the average temperature of the altitude group. The formula is: Where T 异常 For the abnormal fan unit, is the average grouping of this height grouping.

6. A wind turbine generator bearing high temperature fault diagnosis system according to claim 1, characterized in that: Generate an instruction containing a speed adjustment value, and use a communication protocol to send the instruction to the wind turbine with abnormal temperature to ensure that the instruction can be correctly received and executed by the wind turbine.

7. A wind turbine generator bearing high temperature fault diagnosis system according to claim 6, characterized in that: Add the wind turbine number or identifier to the instruction and add the instruction serial number for confirmation and tracking of the instruction.

8. A wind turbine generator bearing high temperature fault diagnosis system according to claim 7, characterized in that: A checksum field is added to the instruction to detect whether an error occurs during the transmission of the instruction. The checksum uses the CRC-16 algorithm to calculate a 16-bit checksum and adds it to the end of the instruction, adding some redundant information to the instruction.

9. A wind turbine generator bearing high temperature fault diagnosis system according to claim 8, characterized in that: When the wind turbine generator system detects a checksum error or inconsistent redundant information in an instruction, it resends the instruction and takes other error handling measures based on the error message.

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