Engine fault detection method

Abstract

The present invention relates to the technical field of fault detection, and more specifically, to an engine fault detection method, comprising: obtaining the concentration sequence of the engine in each historical period, calculating the noise level of each concentration value in the concentration sequence, obtaining the noise distribution of the concentration sequence, wherein the noise level of the concentration value reflects the degree of deviation between the concentration value and the fitting value, and constructing a database based on the noise distribution of all concentration sequences. The present invention screens out the current standard sequence of the concentration sequence of the current period through the noise distribution of each concentration sequence in the database, removes the concentration sequence corresponding to the noise generated by the engine fault, and then selects a more accurate current standard sequence to improve the accuracy of calculating the abnormality degree. In addition, when calculating the fault degree of the engine, the changing trend of the temperature value and concentration value of the engine in the current period is taken into account, which further improves the accuracy of calculating the fault degree of the engine.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and more specifically, to a method for detecting engine faults. Background Art

[0002] Natural gas engines are used in modern industrial production, especially in factory equipment that requires high power and high efficiency. As a clean and efficient energy source, natural gas has gradually replaced traditional oil and coal energy and become one of the important sources of power in the industrial field. In the production process of many factories, equipment often operates cyclically, that is, the equipment needs to be started, stopped and adjusted cyclically according to production needs. For example, in some production lines such as chemical plants, steel plants, and aluminum plants, the natural gas engines used usually need to be started or stopped at specific time intervals to meet the energy needs of production. This kind of cyclically operating equipment often needs to have the ability to start quickly, output stably, and operate efficiently. Therefore, natural gas engines have become the preferred power system due to their fast response speed and high combustion efficiency.

[0003] In the related technology, for example, a Chinese patent application document with publication number CN116241369A discloses an engine fault detection method, a controller and an engine fault detection device. When the engine is in a first operating condition, a methane concentration value is obtained, and the first operating condition is the operating condition of normal engine operation; it is determined whether the methane concentration value is within a preset concentration range; when the methane concentration value is within the preset concentration range, it is determined that a ventilation fault occurs in the engine.

[0004] Currently, after the methane concentration is collected by a methane sensor, the engine is judged whether it has a fault based on the comparison of the methane concentration with the abnormal threshold. However, in some cases, the methane sensor will produce noise during the data collection process or due to normal engine wear, resulting in the collected concentration value being inaccurate. As a result, there is a problem of low accuracy when using concentration values ​​with noisy data to judge engine faults. Summary of the invention

[0005] The present invention provides an engine fault detection method, aiming to solve the problem in the related art that a methane sensor will generate noise influence during the data collection process or the normal wear of the engine, resulting in the collected concentration value being inaccurate, and further there is a problem of low accuracy when using the concentration value with noisy data to judge the engine fault.

[0006] The present invention provides an engine fault detection method, comprising: obtaining a concentration sequence of an engine in each historical period, calculating the noise degree of each concentration value in the concentration sequence, obtaining the noise distribution of the concentration sequence, wherein the noise degree of the concentration value reflects the degree of deviation of the concentration value from a fitting value, and constructing a database based on the noise distribution of all concentration sequences; obtaining the abnormality degree of each concentration value in the concentration sequence of the current period according to the difference between each concentration value in the concentration sequence of the current period and each concentration value in the current standard sequence, and performing weighted summation of each concentration value in the concentration sequence of the current period using the abnormality degree and the noise degree of each concentration value to obtain the fault degree of the engine, and detecting the fault according to the magnitude of the fault degree. The engine is used for fault detection, wherein the fault degree also reflects the change trend of the temperature value and concentration value of the engine in the current cycle; wherein the method for obtaining the current standard sequence includes: clustering the noise distribution of all concentration sequences in the database to obtain multiple first clusters, wherein one first cluster represents a noise distribution type, and clustering all concentration sequences in any first cluster to obtain two second clusters, and calculating the average value of all concentration sequences in the second cluster containing the most concentration sequences as the historical standard sequence corresponding to the noise distribution type of the first cluster, and determining the current standard sequence with the noise distribution type of the current cycle concentration sequence. Accurate fault detection can be provided through multi-dimensional analysis of the engine working state, selection of standard sequences, and weighted evaluation of comprehensive abnormalities and noise.

[0007] Further, the engine fault detection is performed according to the magnitude of the fault, including: if the fault degree is greater than the abnormal threshold, an alarm is issued to alert the staff. By setting an abnormal threshold, when the fault degree exceeds this threshold, the system can issue an alarm in time. This rapid response helps to detect problems in time before a major fault occurs in the engine, avoiding further damage to the engine.

[0008] Further, the current standard sequence of the noise distribution type is determined based on the noise distribution type of the current cycle concentration sequence, including: determining the noise distribution type of the current cycle concentration sequence, including: calculating the similarity between the current cycle concentration sequence and all the historical standard sequences corresponding to the first clustering clusters, and selecting the historical standard sequence with the largest similarity as the current standard sequence, wherein the similarity is the Euclidean distance between the current cycle concentration sequence and the historical standard sequence corresponding to the first clustering cluster. As a simple and intuitive distance metric, the Euclidean distance can clearly reflect the difference between the two sequences, ensuring that the selection of the standard sequence can truly reflect the data characteristics of the current cycle.

[0009] Further, calculate the degree of engine failure , the calculation formula is: ; In the formula, is the concentration sequence of the current cycle concentration value, is the concentration sequence of the current cycle The noise level of each concentration value is Indicates the concentration sequence of the current cycle The abnormality of the concentration value Indicates the number of concentration values ​​in the concentration sequence of the current period, is the standard normalization function.

[0010] Furthermore, the degree of fault also reflects the change trend of the temperature value and concentration value of the engine in the current cycle. The calculation formula is: ; In the formula, It represents the Pearson correlation coefficient between the temperature series of the current cycle and the concentration series of the current cycle, and is used to reflect the changing trend of the temperature and concentration values ​​of the engine in the current cycle. is the concentration sequence of the current cycle concentration value, is the concentration sequence of the current cycle The noise level of each concentration value is Indicates the concentration sequence of the current cycle The abnormality of the concentration value Indicates the number of concentration values ​​in the concentration sequence of the current period, is the standard normalization function.

[0011] Furthermore, the abnormal degree of each concentration value in the concentration sequence of the current period is calculated, and the calculation formula is: ; In the formula, Indicates the concentration sequence of the current engine cycle. The abnormality of the concentration value Indicates the concentration sequence of the current cycle The concentration value of Indicates the current standard sequence The actual value of the concentration value, Represents the mean of all concentration values ​​in the concentration sequence of the current period, Indicates the number of concentration values ​​in the concentration sequence of the current period, Represents the standard normalization function. By comprehensively analyzing the difference between the current cycle concentration sequence and the standard concentration sequence, as well as the degree of fluctuation of the current cycle concentration sequence, normal data and abnormal data can be effectively distinguished.

[0012] Further, the noise level of each concentration value in the concentration sequence is calculated, including: performing least square fitting on the concentration values ​​in the concentration sequence to obtain a first fitting value for each concentration value; removing any concentration value from the concentration sequence, performing least square fitting on the remaining concentration values ​​to obtain a second fitting value for each remaining concentration value; calculating the cumulative sum of differences between the measured value and the first fitting value, the cumulative sum of differences between the measured value and the second fitting value for the remaining concentration values ​​except for any concentration value in the concentration sequence, and calculating the difference between the two cumulative sums of differences, and taking the normalized difference as the noise level of the concentration value. By comparing the difference between the two fitting errors (errors with and without the elimination of the concentration value), the difference can be used to measure the impact of the concentration value on the overall fitting result, thereby obtaining the noise level of each concentration value.

[0013] Furthermore, the noise level of each concentration value in the concentration sequence is calculated using the following formula: ; In the formula, For the The noise level of each concentration value is is the concentration sequence except The concentration value outside The measured value of the concentration, is the concentration sequence except The concentration value outside The first fitted value for the concentration values, is the concentration sequence except The concentration value outside The second fitted value of the concentration value, Represents the number of concentration values ​​in the concentration series.

[0014] Furthermore, the concentration sequence of the engine in each historical period is obtained, including: collecting the concentration value of methane in the engine once at a preset time interval within the period to obtain a concentration sequence composed of multiple concentration values.

[0015] Beneficial Effects

[0016] (I) By calculating the noise level and abnormality level of each concentration value in the current period concentration sequence, the fault level of the calculated engine is weighted to improve the accuracy of the calculation results.

[0017] (ii) Through the noise distribution of each concentration sequence in the database, the current standard sequence of the current period concentration sequence is screened out, the concentration sequence corresponding to the noise generated by the engine failure is eliminated, and then a more accurate current standard sequence is selected to improve the accuracy of calculating the abnormality degree.

[0018] (iii) When calculating the degree of engine failure, the changing trend of the temperature value and concentration value of the engine in the current cycle is taken into account, which further improves the accuracy of calculating the degree of engine failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The following detailed description is read with reference to the accompanying drawings, which illustrate several embodiments of the present invention in an exemplary and non-limiting manner, and in which like or corresponding reference numerals represent like or corresponding parts, wherein:

[0020] Figure 1 is a flowchart schematically illustrating construction of a database according to an embodiment of the present invention;

[0021] Figure 2 is a flowchart schematically illustrating calculation of an engine failure degree according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] like Figure 1 As shown, S110: acquiring the operating data of the engine.

[0025] Specifically, the operation data of the engine is obtained, wherein the operation data may be methane concentration, temperature, or exhaust pressure and intake pressure, etc. The operation data of this embodiment takes temperature and methane concentration as examples. Specifically, during the operation cycle of the engine, the methane concentration in the engine is collected once at a preset time interval using a methane sensor, and a concentration sequence consisting of multiple concentration values ​​within the cycle is obtained. Similarly, the temperature of the engine is collected once at a preset time interval using a temperature sensor, and a temperature sequence consisting of multiple temperature values ​​within the cycle is obtained for subsequent use, wherein the preset time interval may be 1 second or 2 seconds, etc., which may be adjusted according to the specific implementation situation.

[0026] S111: Calculate the noise level of each concentration value in each concentration sequence and construct a database.

[0027] In one embodiment, based on the previous engine operation cycle, the concentration sequence of the engine in each historical cycle is obtained, and the noise level of each concentration value in all concentration sequences is calculated, and then the noise distribution of each concentration sequence is obtained, wherein the noise level of the concentration value reflects the degree of deviation of the concentration value from the fitting value. By comparing the difference between the two fitting errors (the error with and without eliminating the concentration value), the difference can be used to measure the influence of the concentration value on the overall fitting result. If the difference is large, it means that the elimination of the concentration value significantly changes the fitting result, indicating that the concentration value may be noise. Specifically, the concentration values ​​in the concentration sequence are fitted with the least squares method to obtain the first fitting value of each concentration value; any concentration value is eliminated from the concentration sequence, and the remaining concentration values ​​are fitted with the least squares method to obtain the second fitting value of the remaining concentration values; the remaining concentration values ​​except any concentration value in the concentration sequence are calculated, the cumulative sum of the difference between the actual measured value and the first fitting value, the cumulative sum of the difference between the actual measured value and the second fitting value, and the difference between the two cumulative sums of the difference is calculated, and the normalized difference is used as the noise performance level of the concentration value.

[0028] In one embodiment, the noise level of each concentration value in the concentration sequence is calculated, and a calculation method is also provided, as shown below: ; In the formula, For the The noise level of each concentration value is Divide the concentration value in the concentration sequence by Other than The measured value of the concentration, Divide the concentration value in the concentration sequence by Other than The first fitted value of the concentration value, Divide the concentration value in the concentration sequence by Other than The second fitted value of the concentration value, represents the number of concentration values ​​in the concentration sequence, is the standard normalization function. At this point, the noise level of each concentration value in each concentration sequence can be obtained, the noise distribution of the concentration sequence can be obtained, and a database can be constructed based on the noise distribution of all concentration sequences.

[0029] like Figure 2 As shown, S121: calculate the noise level of the current period concentration sequence and determine the current standard sequence.

[0030] Specifically, to calculate the degree of engine failure, it is necessary to obtain the operating data of the engine in the current cycle, that is, the operating data in the latest engine operating cycle, and then obtain the current cycle concentration sequence and the current cycle temperature sequence, so as to calculate the degree of engine failure after the latest use of the engine.

[0031] In one embodiment, the noise level of the concentration sequence of the current period is calculated using the above method for calculating the noise level of each concentration value in the concentration sequence.

[0032] S122: Calculate the abnormality level of each concentration value in the concentration sequence of the current period.

[0033] Specifically, when the engine load and working environment remain unchanged, the difference between the engine's operating data and the standard data at the same time should be small. When the current operating data and the standard data at the same time are significantly different, it means that the current operating data may be abnormal, which also means that the engine is affected by a fault. Therefore, the degree of abnormality of each concentration value in the current cycle concentration sequence can be calculated based on the above characteristics.

[0034] It should be noted that, due to the long-term use of the engine, the noise distribution of the internal parts of the engine will be normal wear and tear, and there will also be noise distribution when the engine fails. When selecting standard data, the concentration sequence corresponding to the noise distribution under normal wear should be selected as the current standard sequence of the current period concentration sequence.

[0035] Specifically, after obtaining the database constructed by the noise distribution of all concentration sequences, it is necessary to classify the noise distribution of all concentration sequences in the database, obtain multiple noise distribution types, and obtain the historical standard sequences corresponding to various noise distribution types. Then determine the noise distribution type of the current period concentration sequence, and thereby determine the current standard sequence corresponding to the current period concentration sequence.

[0036] In one embodiment, the noise distribution of all concentration sequences in the database is clustered to obtain multiple first clusters, wherein one first cluster represents a noise distribution type, and then the noise generated by normal wear of internal engine parts and the noise generated by engine failure in each noise distribution type are distinguished. Specifically, all concentration sequences in any first cluster are clustered to obtain two second clusters, and the average value of all concentration sequences in the second cluster containing the most concentration sequences is used as the historical standard sequence of the noise distribution type corresponding to the first cluster. The reason is that during the operation of the engine, the probability of engine failure is small, while the noise generated by engine wear is much greater than the probability of engine failure. Therefore, the second cluster containing the most concentration sequences is selected as the concentration sequence generated by normal wear, and the second cluster containing fewer concentration sequences is selected as the concentration sequence generated by engine failure. Finally, the current standard sequence is determined by the noise distribution type of the current period concentration sequence. Among them, the clustering method adopts the K-means clustering algorithm.

[0037] Exemplarily, the noise distributions of all concentration sequences in the database are clustered to obtain a plurality of first clusters, namely, first clusters , the first cluster , the first cluster and the first cluster , where the first cluster The corresponding noise distribution type is , the first cluster The corresponding noise distribution type is , the first cluster The corresponding noise distribution type is and the first cluster The corresponding noise distribution type is Then the noise distribution type is calculated as The corresponding historical standard sequence is , the noise distribution type is The corresponding historical standard sequence is , the noise distribution type is The corresponding historical standard sequence is , the noise distribution type is The corresponding historical standard sequence is .

[0038] In one embodiment, the current standard sequence is determined based on the noise distribution type of the current periodic concentration sequence. The method first includes determining the noise distribution type of the current periodic concentration sequence. Specifically, the similarity between the current periodic concentration sequence and all historical standard sequences corresponding to the first clustering clusters is calculated, and the historical standard sequence with the greatest similarity is selected as the current standard sequence, wherein the similarity is the Euclidean distance between the current periodic concentration sequence and the historical standard sequence corresponding to the first clustering cluster.

[0039] For example, the Euclidean distance algorithm is used to calculate the current period concentration sequence and the historical standard sequence , Historical Standard Series , Historical Standard Series and historical standard series The similarities between them are , , , ,and , and finally select the similarity The corresponding historical standard sequence is Serves as the current standard sequence for the concentration sequence of the current cycle.

[0040] The current standard sequence of the current period concentration sequence is selected by the above method, and the concentration sequence corresponding to the noise generated by the engine failure is eliminated, so as to select the accurate current standard sequence, thereby improving the accuracy of the subsequent calculation of the abnormal degree of each concentration value in the current period concentration sequence.

[0041] In one embodiment, the abnormality degree of each concentration value in the concentration sequence of the current period is calculated, and the calculation formula is: ; In the formula, Indicates the concentration sequence of the current engine cycle. The abnormality of the concentration value Indicates the concentration sequence of the current cycle The concentration value of Indicates the current standard sequence The actual value of the concentration value, Represents the mean of all concentration values ​​in the concentration sequence of the current period, Indicates the number of concentration values ​​in the concentration sequence of the current period, represents the standard normalization function. Among them, Indicates the concentration sequence of the current cycle concentration value and the current standard sequence The difference between the concentration values, the greater the difference, the The greater the abnormality of the concentration value. Indicates the degree of dispersion of the concentration values ​​in the concentration sequence of the current period. The greater the dispersion, the The larger the value of The greater the abnormality of the concentration value.

[0042] S123: Calculate the degree of engine failure.

[0043] In one embodiment, the abnormality and noise levels of each concentration value are used to perform weighted summation on each concentration value in the current period concentration sequence to obtain the engine failure degree, and the calculation formula is: ; In the formula, is the concentration sequence of the current cycle concentration value, is the concentration sequence of the current cycle The noise level of each concentration value is Indicates the concentration sequence of the current cycle The abnormal degree of the concentration value, N represents the number of concentration values ​​in the concentration sequence of the current period, is the standard normalization function. Among them, The larger the value of The greater the possibility of noise in the first concentration value, the Concentration value The less credible, the more Concentration value A smaller weight makes it possible to calculate the i-th concentration value The value of the engine fault degree at the moment is low, thereby reducing the impact of the data collected at this moment on the fault degree of the overall moment.

[0044] It should be noted that when the methane concentration of the engine is higher, the possibility of engine failure is greater, but it is inaccurate to judge the engine failure simply by the methane concentration. Specifically, when the methane concentration of the engine is higher and the engine temperature is higher, the probability of engine abnormality is greater. When the methane concentration of the engine is higher and the engine temperature is lower, the probability of engine abnormality is lower. The reason is that higher temperatures usually help to reduce methane concentrations, indicating that the combustion process is more complete. If the methane concentration is higher at this time, the probability of engine abnormality is greater; that is, when the engine temperature is higher, the methane concentration of the engine should be lower to meet the normal operation of the motor. Therefore, according to the changes in the methane concentration, temperature and related linear relationships of the engine, and combined with the abnormality and noise levels of each concentration value, the following another embodiment is provided to calculate the degree of engine failure.

[0045] In another embodiment, the degree of engine failure is calculated , the calculation formula is: ; In the formula, It represents the Pearson correlation coefficient between the temperature series of the current cycle and the concentration series of the current cycle, and is used to reflect the changing trend of the temperature and concentration values ​​of the engine in the current cycle. is the concentration sequence of the current cycle concentration value, is the concentration sequence of the current cycle The noise level of the concentration value, N represents the number of concentration values ​​in the current cycle concentration sequence, is the standard normalization function.

[0046] At this point, the fault degree of the engine is obtained, and the engine fault detection is performed according to the magnitude of the fault degree. Specifically, if the fault degree of the engine is greater than the abnormal threshold, an alarm is issued to alert the staff and remind the staff to repair it. If the fault degree of the engine is less than or equal to the abnormal threshold, no alarm is issued and normal operation is sufficient. In this embodiment, the empirical value of the abnormal threshold is 0.7. In other embodiments, the empirical value of the abnormal threshold can also be 0.77 or 0.8, etc., which can be adjusted according to the specific implementation situation.

[0047] Through the above steps, the noise level and abnormality level of each concentration value in the current cycle concentration sequence are calculated, which are used to finally weight the engine fault level, thereby improving the accuracy of the calculation results. And through the noise distribution of each concentration sequence in the database, the current standard sequence corresponding to the current cycle concentration sequence is screened out, and the concentration sequence corresponding to the noise generated by the engine fault is eliminated, and then a more accurate current standard sequence is selected to improve the accuracy of the calculation of the abnormality level. When calculating the engine fault level, the changing trend of the engine temperature value and concentration value in the current cycle is considered, further improving the accuracy of calculating the engine fault level.

[0048] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0049] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0050] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. An engine fault detection method, characterized in that: include: The concentration value of methane in the engine is collected at a preset time interval within the period, and a concentration sequence consisting of multiple concentration values ​​is obtained as the concentration sequence of the engine in each historical period; Calculate the noise level of each concentration value in the concentration sequence, including: Performing least square fitting on the concentration values ​​in the concentration sequence to obtain a first fitting value of each concentration value; removing any concentration value from the concentration sequence, performing least square fitting on the remaining concentration values ​​to obtain a second fitting value of each remaining concentration value; Calculating the remaining concentration values ​​excluding any concentration value in the concentration sequence, the cumulative sum of differences between the actual measured value and the first fitted value, and the cumulative sum of differences between the actual measured value and the second fitted value, and calculating the difference between the two cumulative sums of differences, and taking the normalized difference as the noise degree of the concentration value to obtain the noise distribution of the concentration sequence; wherein the noise degree of the concentration value reflects the degree of deviation of the concentration value from the fitted value, and constructing a database based on the noise distribution of all concentration sequences; According to the difference between each concentration value in the current period concentration sequence and each concentration value in the current standard sequence, the abnormality degree of each concentration value in the current period concentration sequence is obtained, and the abnormality degree and noise degree of each concentration value are used to perform weighted summation on each concentration value in the current period concentration sequence to obtain the fault degree of the engine, and the engine fault detection is performed according to the magnitude of the fault degree; The method for obtaining the current standard sequence includes: clustering the noise distribution of all concentration sequences in the database to obtain multiple first clusters, wherein one first cluster represents a noise distribution type, and clustering all concentration sequences in any first cluster to obtain two second clusters, and calculating the average value of all concentration sequences in the second cluster containing the most concentration sequences as the historical standard sequence corresponding to the noise distribution type of the first cluster, and determining the current standard sequence based on the noise distribution type of the current period concentration sequence.

2. The engine fault detection method according to claim 1, characterized in that: Performing fault detection on the engine according to the degree of the fault includes: If the fault level is greater than an abnormal threshold, an alarm is issued to alert the staff.

3. The engine failure detection method according to claim 1, characterized in that: The current standard sequence of noise distribution type is determined by the noise distribution type of the current period concentration sequence, including: Determining the noise distribution type of the current cycle concentration sequence includes: calculating the similarity between the current cycle concentration sequence and all historical standard sequences corresponding to the first cluster clusters, and selecting the historical standard sequence with the largest similarity as the current standard sequence, wherein the similarity is the Euclidean distance between the current cycle concentration sequence and the historical standard sequence corresponding to the first cluster cluster.

4. The engine failure detection method according to claim 1, characterized in that: Calculate the degree of engine failure , the calculation formula is: ； In the formula, is the concentration sequence of the current cycle concentration value, is the concentration sequence of the current cycle The noise level of each concentration value is Indicates the concentration sequence of the current cycle The abnormality of the concentration value Indicates the number of concentration values ​​in the concentration sequence of the current period, is the standard normalization function.

5. The engine fault detection method according to claim 1, characterized in that: The fault degree also reflects the change trend of the temperature value and concentration value of the engine in the current cycle. The calculation formula is: ； In the formula, It represents the Pearson correlation coefficient between the temperature series of the current cycle and the concentration series of the current cycle, and is used to reflect the changing trend of the temperature and concentration values ​​of the engine in the current cycle. is the concentration sequence of the current cycle concentration value, is the concentration sequence of the current cycle The noise level of each concentration value is Indicates the concentration sequence of the current cycle The abnormality of the concentration value Indicates the number of concentration values ​​in the concentration sequence of the current period, is the standard normalization function.

6. The engine fault detection method according to claim 1, characterized in that: Calculate the abnormal degree of each concentration value in the current period concentration sequence. The calculation formula is: ； In the formula, Indicates the concentration sequence of the current engine cycle. The abnormality of the concentration value Indicates the concentration sequence of the current cycle The concentration value of Indicates the current standard sequence The actual value of the concentration value, Represents the mean of all concentration values ​​in the concentration sequence of the current period, Indicates the number of concentration values ​​in the concentration sequence of the current period, Represents the standard normalization function.

7. The engine fault detection method according to claim 1, characterized in that: Calculate the noise level of each concentration value in the concentration sequence. The calculation formula is: ； In the formula, For the The noise level of each concentration value is is the concentration sequence except The concentration value outside The measured value of the concentration, is the concentration sequence except The concentration value outside The first fitted value for the concentration value, is the concentration sequence except The concentration value outside The second fitted value of the concentration value, represents the number of concentration values ​​in the concentration sequence, is the standard normalization function.

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