PLC controller fault detection system and method thereof

Abstract

The invention discloses a PLC controller fault detection system and method, and relates to the technical field of fault detection regulation and control, and the method comprises the steps: providing a known digital input signal, and obtaining an output response; constructing a fault representation set of the output response; the characterization fitting degree between the PLC and each fault type is determined; determining the interaction relevance between any two fault types of the PLC; based on the interaction relevance between the fault types, constructing a fault relevance model for each fault type of the PLC; and performing fault diagnosis on the PLC based on the fault correlation value of each fault type of the PLC in the current application environment. The method has the advantages that the fault type of the PLC can be effectively recognized, and efficient and accurate fault diagnosis of the PLC can be achieved.

Description

Technical Field

[0001] This invention relates to the field of fault detection technology, specifically to a PLC controller fault detection system and method. Background Technology

[0002] As the core of modern industrial automation control systems, the reliability of PLC operation is crucial to production efficiency, equipment safety, and cost control. However, the complex industrial environment, including strong electromagnetic interference, temperature and humidity variations, dust, and vibration, inevitably leads to various hardware or software failures in PLCs during long-term operation. Traditional fault detection methods mainly rely on engineers' experience, simple comparison of input and output signals, or logical analysis based on preset rules. These methods often have significant limitations when facing complex and coupled fault phenomena: on the one hand, a single fault manifestation may correspond to multiple potential fault types, making precise location difficult; on the other hand, a single initial fault point may trigger a chain reaction, producing multi-level, non-intuitive manifestations. For example, power fluctuations may cause CPU malfunctions, which in turn affect the communication module, ultimately resulting in multiple seemingly independent output anomalies. Existing methods mostly view each fault type in isolation, lacking effective modeling and analysis of the potential correlations between faults, which can easily lead to misdiagnosis or missed diagnosis, prolonging downtime for maintenance. Summary of the Invention

[0003] To address the aforementioned technical problems, a PLC controller fault detection system and method are provided. This technical solution solves the problem that most existing methods view each fault type in isolation, lack effective modeling and analysis of the potential correlation between faults, which can easily lead to misdiagnosis or missed diagnosis and prolong downtime for maintenance.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A PLC controller fault detection method, comprising:

[0006] Use a signal generator to provide a known digital input signal to the PLC controller and obtain the output response of the PLC controller;

[0007] Observe the output response of the PLC controller, obtain several fault characteristics in the output response, and construct a fault characteristic set of the output response;

[0008] By combining the fault characterization set of the output response with the known PLC controller fault type library, the characterization fit between the fault characterization set of the PLC controller in the current application environment and each fault type is determined.

[0009] Based on historical fault analysis experience, determine the interactive correlation between any two fault types of the PLC controller;

[0010] Based on the interactive correlation between fault types, a fault correlation model is constructed for each fault type of the PLC controller. The fault correlation model takes the degree of fit between the fault representation set and each fault type as input and the fault correlation value of each fault type occurring in the PLC controller in the current application environment as output.

[0011] Based on the fault-related values ​​of each type of fault that occurs in the PLC controller under the current application environment, fault diagnosis is performed on the PLC controller.

[0012] Preferably, determining the degree of fit between the fault characterization set of the PLC controller in the current application environment and each fault type, by combining the fault characterization set of the output response and the known PLC controller fault type library, specifically includes:

[0013] Based on the known PLC controller fault type library, determine the standard characterization library corresponding to each fault type;

[0014] The ratio of the number of elements in the intersection of the fault characterization set of the output response and the standard characterization library corresponding to the fault type to the number of elements in the union of the fault characterization set of the output response and the standard characterization library corresponding to the fault type is the characterization fit between the fault characterization set and the fault type of the PLC controller in the current application environment.

[0015] The model iterates through and calculates the degree of fit between the set of fault characteristics of the PLC controller in the current application environment and each fault type.

[0016] Preferably, determining the interaction correlation between any two fault types of the PLC controller based on historical fault analysis experience specifically includes:

[0017] Construct a set of fault types A for the PLC controller, A = {a1…a2} i …a n}, where a i Let be the i-th fault type of the PLC controller, and n be the total number of fault types of the PLC controller;

[0018] Determine the type of fault (a) i Will it lead to type a of failure? j If it occurs, then record the fault type as 'a'. j Fault type a i Associated fault types, fault type a i Fault type a j The causes of the fault are identified; if not, then fault type a is identified. j and fault type a i There is no interactive relationship;

[0019] Filter out fault type a i All associated fault types, construct fault type a i Interactive association set A i A i ={a i1 …a ij …a ini}, where a ij Fault type a i The j-th associated fault type, ni is the fault type a i The total number of associated fault types;

[0020] Construct an interaction association set for each fault type.

[0021] Preferably, the step of constructing a fault association model for each fault type of the PLC controller based on the interactive correlation between fault types specifically includes:

[0022] Determine the type of fault a i The goodness of fit of the representation is denoted as b. i Determine A i The goodness of fit of each element in the model is used to construct the fault type a. i The set B of the correlation characterization fit i B i ={b i1 …b ij …b ini}, where b ij Fault type a i The goodness of fit of the representation of the j-th associated fault type;

[0023] Based on the correlation fitting formula, the fault type a is calculated. i The correlation fit value P i ;

[0024] Determine P i With b i If the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i .

[0025] Preferably, the correlation fitting formula is as follows:

[0026]

[0027] In the formula, α is the correlation coefficient, and the value of α ranges from 0.1 to 0.5. M j For b ij The total number of types of malfunctions that cause problems.

[0028] Preferably, the fault diagnosis of the PLC controller based on the fault correlation value of each type of fault occurring in the current application environment specifically includes:

[0029] Determine if there are any fault types with fault correlation values ​​greater than a preset correlation threshold. If so, record all fault types with fault correlation values ​​greater than the preset correlation threshold as faults to be diagnosed, and take preventive measures for each fault to be diagnosed in order of the magnitude of the fault correlation values. If not, take the fault type corresponding to the maximum fault correlation value as the fault to be diagnosed, and take preventive measures for the fault to be diagnosed.

[0030] Furthermore, a PLC controller fault detection system is proposed to implement the PLC controller fault detection method described above, including:

[0031] The storage module is used to store a library of known PLC controller fault types;

[0032] A signal generator that provides a known digital input signal to the PLC controller;

[0033] The fault characterization analysis module is electrically connected to the storage module. The fault characterization analysis module is used to observe the output response of the PLC controller, obtain several fault characterizations in the output response, construct a fault characterization set of the output response, and combine the fault characterization set of the output response with the known PLC controller fault type library to determine the characterization fit degree between the fault characterization set of the PLC controller in the current application environment and each fault type.

[0034] The correlation analysis module is used to determine the interactive correlation between any two fault types of the PLC controller based on historical fault analysis experience.

[0035] The fitting analysis module is electrically connected to the fault characterization analysis module and the correlation analysis module. The fitting analysis module is used to construct a fault correlation model for each fault type of the PLC controller based on the interactive correlation between fault types, and to calculate the fault correlation value of each fault type based on the fault correlation model of each fault type.

[0036] A diagnostic output unit is electrically connected to the fitting analysis module. The diagnostic output unit is used to diagnose faults in the PLC controller based on the fault correlation values ​​of each type of fault that occurs in the current application environment.

[0037] Optionally, the fitting analysis module includes:

[0038] The correlation fitting unit is used to calculate the fault type a based on the correlation fitting formula. i The correlation fit value P i ;

[0039] The loop unit is used to determine P. i With b i If the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i .

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] This invention proposes a PLC controller fault detection scheme. By calculating the degree of fit between the fault representation set in the actual environment and the standard fault types, and innovatively introducing and modeling the interactive correlation between fault types based on historical experience, a fault correlation model considering the correlation effect is constructed for each fault type. Finally, the fault correlation value that reflects the probability of the real fault is output. This method significantly improves the accuracy and robustness of PLC fault diagnosis in complex industrial environments, effectively solving the problems of misdiagnosis due to single feature matching and missed diagnosis due to ignoring the fault chain effect. It can accurately locate the root cause fault and identify potential related faults, and supports the concurrent diagnosis of multiple faults. This allows for the development of efficient diagnostic strategies, thereby greatly reducing troubleshooting time and downtime losses, and lowering maintenance costs. Attached Figure Description

[0042] Figure 1 This is a flowchart of the PLC controller fault detection method proposed in this solution;

[0043] Figure 2This is a flowchart of the method proposed in this scheme for determining the characterization fit between PLC controllers and fault types;

[0044] Figure 3 This is a flowchart of the method for fault diagnosis of PLC controllers proposed in this solution. Detailed Implementation

[0045] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0046] Reference Figure 1 As shown, a diagnostic method for PLC controller fault detection includes:

[0047] Use a signal generator to provide a known digital input signal to the PLC controller and obtain the output response of the PLC controller;

[0048] Observe the output response of the PLC controller, obtain several fault characteristics in the output response, and construct a fault characteristic set of the output response;

[0049] By combining the fault characterization set of the output response with the known PLC controller fault type library, the characterization fit between the fault characterization set of the PLC controller in the current application environment and each fault type is determined.

[0050] Based on historical fault analysis experience, determine the interactive correlation between any two fault types of the PLC controller;

[0051] Based on the interactive correlation between fault types, a fault correlation model is constructed for each fault type of the PLC controller. The fault correlation model takes the degree of fit between the fault representation set and each fault type as input and the fault correlation value of each fault type in the current application environment of the PLC controller as output.

[0052] Based on the fault-related values ​​of each type of fault that occurs in the PLC controller under the current application environment, fault diagnosis is performed on the PLC controller.

[0053] During the fault identification phase, the causal relationship between various fault types is fully considered. When identifying fault types, the correlation between fault types is iterated to achieve high-precision and accurate positioning of the factors causing PLC controller faults. This enables rapid and accurate fault diagnosis of PLC controllers and improves the diagnostic efficiency of PLC controllers.

[0054] Reference Figure 2As shown, by combining the fault characterization set of the output response and the known PLC controller fault type library, the degree of fit between the fault characterization set of the PLC controller in the current application environment and each fault type is determined, specifically including:

[0055] Based on the known PLC controller fault type library, determine the standard characterization library corresponding to each fault type;

[0056] The ratio of the number of elements in the intersection of the fault characterization set of the output response and the standard characterization library corresponding to the fault type to the number of elements in the union of the fault characterization set of the output response and the standard characterization library corresponding to the fault type is the characterization fit between the fault characterization set and the fault type of the PLC controller in the current application environment.

[0057] The model iterates through and calculates the degree of fit between the set of fault characteristics of the PLC controller in the current application environment and each fault type.

[0058] The main characteristics of a PLC controller's faults lie in the difference between its output signal and the ideal signal. These differences may manifest as deviations in the amplitude, phase, or frequency of the output signal, or as waveform distortion or increased noise. Specifically, the fault characteristics of a PLC controller may include the following aspects:

[0059] Output signal amplitude deviation: The actual output signal amplitude of the PLC controller may deviate from the expected ideal value. This deviation may be caused by factors such as gain fault, offset fault, or nonlinear fault.

[0060] Output signal waveform distortion: The output signal waveform of the PLC controller may deviate from the ideal sine wave or other waveforms, manifesting as waveform irregularities, increased harmonic components, etc. This distortion may be caused by factors such as the nonlinear characteristics of the PLC controller, circuit asymmetry, or external interference.

[0061] Increased output signal noise: The output signal of a PLC controller may contain additional noise components. This noise may originate from internal circuit noise within the PLC controller, electromagnetic interference from the external environment, or power supply noise. Increased noise reduces the signal-to-noise ratio and dynamic range of the PLC controller, thus affecting its performance.

[0062] In this scheme, the Jaccard similarity coefficient between the fault characterization of the PLC controller output result under the current environment and the standard fault characterization of various fault types is obtained by fitting the known signal output result to the PLC controller. This coefficient reflects the natural fit value of the possible fault types of the PLC controller under the current environment.

[0063] Based on historical fault analysis experience, determining the interaction correlation between any two fault types of a PLC controller specifically includes:

[0064] Construct a set of fault types A for the PLC controller, A = {a1…a2} i …a n}, where a i Let be the i-th fault type of the PLC controller, and n be the total number of fault types of the PLC controller;

[0065] Determine the type of fault (a) i Will it lead to type a of failure? j If it occurs, then record the fault type as 'a'. j Fault type a i Associated fault types, fault type a i Fault type a j The causes of the fault are identified; if not, then fault type a is identified. j and fault type a i There is no interactive relationship;

[0066] Filter out fault type a i All associated fault types, construct fault type a i Interactive association set A i A i ={a i1 …a ij …a ini}, where a ij Fault type a i The j-th associated fault type, ni is the fault type a i The total number of associated fault types;

[0067] Construct an interaction association set for each fault type.

[0068] Because there may be multiple correlations between the fault types of PLC controllers, such as the output of the PLC controller drifting with temperature changes, which may lead to gain faults, this solution fully considers the correlation between these faults and conducts a comprehensive analysis of the fault types.

[0069] Based on the interactive correlation between fault types, a fault correlation model is constructed for each fault type of the PLC controller, specifically including:

[0070] Determine the type of fault a i The goodness of fit of the representation is denoted as b. i Determine A i The goodness of fit of each element in the model is used to construct the fault type a. iThe set B of the correlation characterization fit i B i ={b i1 …b ij …b ini}, where b ij Fault type a i The goodness of fit of the representation of the j-th associated fault type;

[0071] Based on the correlation fitting formula, the fault type a is calculated. i The correlation fit value P i ;

[0072] Determine P i With b i If the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i .

[0073] The specific formula for correlation fitting is as follows:

[0074]

[0075] In the formula, α is the correlation coefficient, and the value of α ranges from 0.1 to 0.5. M j For b ij The total number of causal fault types, where the value of α is determined by the performance of the PLC controller. For PLC controllers with high linearity and high stability, the correlation between various faults is relatively independent, the correlation effect is small, and the value of the correlation coefficient decreases. Conversely, the value of the correlation coefficient increases.

[0076] In this scheme, based on the PAGERANK algorithm, an association fitting formula is constructed. The characterization fitting degree of each fault type is used as the initial fitting value of each fault type. Combined with the causal correlation between each fault type of the MCU digital simulator, the link relationship between fault types is constructed. Then, the association fitting formula is used to deeply explore the deep correlation between each fault type and the current fault characterization, so as to achieve high-precision and accurate positioning of the fault-causing factors of PLC controller.

[0077] Through several iterations of the above fault association model, the association fitting values ​​of all fault types tend to stabilize. The association fitting value of this temperature fully considers the natural similarity between the fault type and the existing fault performance, as well as the interactive association results between various faults, and can accurately identify the fault type that matches the current fault performance.

[0078] Reference Figure 3 As shown, based on the fault-related values ​​of each fault type occurring in the PLC controller under the current application environment, fault diagnosis of the PLC controller specifically includes:

[0079] Determine if there are any fault types with fault correlation values ​​greater than a preset correlation threshold. If so, record all fault types with fault correlation values ​​greater than the preset correlation threshold as faults to be diagnosed, and take preventive measures for each fault to be diagnosed in order of the magnitude of the fault correlation values. If not, take the fault type corresponding to the maximum fault correlation value as the fault to be diagnosed, and take preventive measures for the fault to be diagnosed.

[0080] Furthermore, based on the same inventive concept as the aforementioned PLC controller fault detection diagnostic method, this solution also proposes a PLC controller fault detection system, comprising:

[0081] The storage module is used to store a library of known PLC controller fault types.

[0082] A signal generator provides a known digital input signal to the PLC controller.

[0083] The fault characterization analysis module is electrically connected to the storage module. The fault characterization analysis module is used to observe the output response of the PLC controller, obtain several fault characterizations in the output response, construct a fault characterization set of the output response, and combine the fault characterization set of the output response with the known PLC controller fault type library to determine the characterization fit degree between the fault characterization set of the PLC controller in the current application environment and each fault type.

[0084] The correlation analysis module is used to determine the interactive correlation between any two fault types of a PLC controller based on historical fault analysis experience.

[0085] The fitting analysis module is electrically connected to the fault characterization analysis module and the correlation analysis module. The fitting analysis module is used to build a fault correlation model for each fault type of the PLC controller based on the interactive correlation between fault types, and to calculate the fault correlation value of each fault type based on the fault correlation model of each fault type.

[0086] The diagnostic output unit is electrically connected to the fitting analysis module. The diagnostic output unit is used to diagnose faults in the PLC controller based on the fault-related values ​​of each type of fault that occurs in the current application environment.

[0087] The fitting analysis module includes:

[0088] The correlation fitting unit is used to calculate the fault type a based on the correlation fitting formula. i The correlation fit value P i ;

[0089] The loop unit is used to determine P. i With b i If the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i .

[0090] The usage process of the above system is as follows:

[0091] Step 1: The signal generator provides the PLC controller with a known digital input signal;

[0092] Step 2: The fault characterization analysis module observes the output response of the PLC controller, obtains several fault characterizations in the output response, constructs a fault characterization set of the output response, and combines the fault characterization set of the output response with the known PLC controller fault type library to determine the characterization fit between the fault characterization set of the PLC controller in the current application environment and each fault type.

[0093] Step 3: Based on historical fault analysis experience, the correlation analysis module determines the interactive correlation between any two fault types of the PLC controller;

[0094] Step 4: The correlation fitting unit calculates the fault type a based on the correlation fitting formula. i The correlation fit value P i ;

[0095] Step 5: Loop unit checks P i With b iIf the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i ;

[0096] Step Six: The diagnostic output unit performs fault diagnosis on the PLC controller based on the fault-related values ​​of each type of fault that occurs in the current application environment.

[0097] In summary, the advantages of this invention are as follows: by calculating the degree of fit between the fault representation set in the actual environment and the standard fault type, and by innovatively introducing and modeling the interactive correlation between fault types based on historical experience, a fault correlation model considering the correlation effect is constructed for each fault type, and finally the fault correlation value that reflects the actual probability of the fault is output. This method significantly improves the accuracy and robustness of PLC fault diagnosis in complex industrial environments, effectively solves the problems of misdiagnosis due to single feature matching and missed diagnosis due to ignoring the fault chain effect, can accurately locate the root cause fault and identify potential related faults, supports the concurrent diagnosis of multiple faults, and provides a basis for formulating efficient diagnostic strategies, thereby greatly reducing troubleshooting time and downtime losses and reducing maintenance costs.

[0098] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for detecting faults in a PLC controller, characterized in that, include: Use a signal generator to provide a known digital input signal to the PLC controller and obtain the output response of the PLC controller; Observe the output response of the PLC controller, obtain several fault characteristics in the output response, and construct a fault characteristic set of the output response; By combining the fault characterization set of the output response with the known PLC controller fault type library, the characterization fit between the fault characterization set of the PLC controller in the current application environment and each fault type is determined. Based on historical fault analysis experience, determine the interactive correlation between any two fault types of the PLC controller; Based on the interactive correlation between fault types, a fault correlation model is constructed for each fault type of the PLC controller. The fault correlation model takes the degree of fit between the fault representation set and each fault type as input and the fault correlation value of each fault type occurring in the PLC controller in the current application environment as output. Based on the fault-related values ​​of each type of fault that occurs in the PLC controller under the current application environment, fault diagnosis is performed on the PLC controller.

2. The PLC controller fault detection method according to claim 1, characterized in that, The determination of the degree of fit between the fault characterization set of the PLC controller in the current application environment and each fault type, which combines the fault characterization set of the output response with the known PLC controller fault type library, specifically includes: Based on the known PLC controller fault type library, determine the standard characterization library corresponding to each fault type; The ratio of the number of elements in the intersection of the fault characterization set of the output response and the standard characterization library corresponding to the fault type to the number of elements in the union of the fault characterization set of the output response and the standard characterization library corresponding to the fault type is the characterization fit between the fault characterization set and the fault type of the PLC controller in the current application environment. The model iterates through and calculates the degree of fit between the set of fault characteristics of the PLC controller in the current application environment and each fault type.

3. The PLC controller fault detection method according to claim 2, characterized in that, The determination of the interaction correlation between any two fault types of a PLC controller based on historical fault analysis experience specifically includes: Construct a set of fault types A for the PLC controller, A = {a1…a2} i …a n }, where a i Let be the i-th fault type of the PLC controller, and n be the total number of fault types of the PLC controller; Determine the type of fault (a) i Will it lead to type a of failure? j If it occurs, then record the fault type as 'a'. j Fault type a i Associated fault types, fault type a i Fault type a j The causes of the fault are identified; if not, then fault type a is identified. j and fault type a i There is no interactive relationship; Filter out fault type a i All associated fault types, construct fault type a i Interactive association set A i A i ={a i1 …a ij …a ini }, where a ij Fault type a i The j-th associated fault type, ni is the fault type a i The total number of associated fault types; Construct an interaction association set for each fault type.

4. The PLC controller fault detection method according to claim 3, characterized in that, The construction of a fault association model for each fault type of the PLC controller based on the interactive correlation between fault types specifically includes: Determine the type of fault a i The goodness of fit of the representation is denoted as b. i Determine A i The goodness of fit of each element in the model is used to construct the fault type a. i The set B of the correlation characterization fit i B i ={b i1 …b ij …b ini }, where b ij Fault type a i The goodness of fit of the representation of the j-th associated fault type; Based on the correlation fitting formula, the fault type a is calculated. i The correlation fit value P i ; Determine P i With b i If the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i .

5. A PLC controller fault detection method according to claim 4, characterized in that, The specific correlation fitting formula is as follows: In the formula, α is the correlation coefficient, and the value of α ranges from 0.1 to 0.

5. M j For b ij The total number of types of malfunctions that cause problems.

6. The PLC controller fault detection method according to claim 5, characterized in that, The fault diagnosis of the PLC controller based on the fault-related values ​​of each fault type occurring in the current application environment specifically includes: Determine if there are any fault types with fault correlation values ​​greater than a preset correlation threshold. If so, record all fault types with fault correlation values ​​greater than the preset correlation threshold as faults to be diagnosed, and take preventive measures for each fault to be diagnosed in order of the magnitude of the fault correlation values. If not, take the fault type corresponding to the maximum fault correlation value as the fault to be diagnosed, and take preventive measures for the fault to be diagnosed.

7. A PLC controller fault detection system, characterized in that, The method for implementing the PLC controller fault detection method as described in any one of claims 1-6 includes: The storage module is used to store a library of known PLC controller fault types; A signal generator that provides a known digital input signal to the PLC controller; The fault characterization analysis module is electrically connected to the storage module. The fault characterization analysis module is used to observe the output response of the PLC controller, obtain several fault characterizations in the output response, construct a fault characterization set of the output response, and combine the fault characterization set of the output response with the known PLC controller fault type library to determine the characterization fit degree between the fault characterization set of the PLC controller in the current application environment and each fault type. The correlation analysis module is used to determine the interactive correlation between any two fault types of the PLC controller based on historical fault analysis experience. The fitting analysis module is electrically connected to the fault characterization analysis module and the correlation analysis module. The fitting analysis module is used to construct a fault correlation model for each fault type of the PLC controller based on the interactive correlation between fault types, and to calculate the fault correlation value of each fault type based on the fault correlation model of each fault type. A diagnostic output unit is electrically connected to the fitting analysis module. The diagnostic output unit is used to diagnose faults in the PLC controller based on the fault correlation values ​​of each type of fault that occurs in the current application environment.

8. A PLC controller fault detection system according to claim 7, characterized in that, The fitting analysis module includes: The correlation fitting unit is used to calculate the fault type a based on the correlation fitting formula. i The correlation fit value P i ; The loop unit is used to determine P. i With b i If the deviation value is less than the convergence threshold, then terminate fault type a. i The correlation fit is calculated by assigning the correlation fit value P. i As a type of fault a i The fault-related values, and P i The value assigned to b i If not, then P i The value assigned to b i And based on A i The correlation fitting results for each element are used to further calculate the fault type 'a' using the correlation fitting formula. i The correlation fit value P i .

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