Patent Publication Number: US-10310934-B2

Title: Methods and systems for diagnosing a controller area network

Description:
TECHNICAL FIELD 
     The technical field generally relates controller area networks, and more particularly relates to methods and systems for diagnosing a controller area network. 
     BACKGROUND 
     Vehicles typically include a number of controllers that are communicatively coupled through a bus. A Controller Area Network (CAN) bus is a vehicle bus that allows the controllers and other devices to communicate with each other without a host computer. Communications between controllers on the CAN bus are made according to a message-based protocol, which is designed originally for multiplex electrical wiring within automobiles, but is also used in many other vehicle and non-vehicle applications. 
     It is desirable to monitor the CAN bus for faults. Some monitoring systems monitor CAN bus physical layer signals e.g. measuring voltage for faults using hardware-based approach (voltage sensing circuit). Other monitoring systems monitor CAN messages, i.e. software based approach for faults. 
     Accordingly, it is desirable to provide methods and systems for monitoring the CAN bus using a consolidated approach. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. 
     SUMMARY 
     Methods and system are provided for monitoring a controller area network (CAN) bus. In one embodiment, a method includes: receiving fault data associated with the CAN bus; processing, by a processor, the fault data with software based methods to determine a first propositions set; processing, by the processor, the fault data with hardware based methods to determine a second proposition set; processing, by the processor, the first propositions set and the second proposition set to determine a fault decision; and generating, by the processor, a diagnosis of the CAN bus based on the fault decision. 
     In one embodiment, a system includes a non-transitory computer readable medium. The non-transitory computer readable medium includes a software based solution module that receives fault data associated with the CAN bus, and that processes, by a processor, the fault data with a software based method to determine a first propositions set. The non-transitory computer readable medium further includes a hardware based solution module that processes, by the processor, the fault data with a hardware based method to determine a second proposition set. The non-transitory computer readable medium further includes an evaluation module that processes, by the processor, the first propositions set and the second proposition set to determine a fault decision. The non-transitory computer readable medium further includes a diagnosis module that, by the processor, generates a diagnosis of the CAN bus based on the fault decision. 
     In one embodiment, a vehicle includes a plurality of controllers communicatively coupled via a controller area network bus, wherein at least one of the controllers comprises a non-transitory computer readable medium. The non-transitory computer readable medium includes a software based solution module that receives fault data associated with the CAN bus, and that processes, by a processor, the fault data with a software based method to determine a first propositions set. The non-transitory computer readable medium further includes a hardware based solution module that processes, by the processor, the fault data with a hardware based method to determine a second proposition set. The non-transitory computer readable medium further includes an evaluation module that processes, by the processor, the first propositions set and the second proposition set to determine a fault decision. The non-transitory computer readable medium further includes a diagnosis module that, by the processor, generates a diagnosis of the CAN bus based on the fault decision. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       The present disclosure will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein: 
         FIG. 1  is a block diagram illustrating a vehicle including a controller area network (CAN) bus and a monitoring system in accordance with various embodiments; 
         FIG. 2  is a dataflow diagram illustrating the monitoring system in accordance with exemplary embodiments; and 
         FIG. 3  is a flowchart illustrating a method that may be performed by the monitoring system in accordance with exemplary embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. As used herein, the term module refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
     Embodiments of the present disclosure may be described herein in terms of functional and/or logical block components and various processing steps. It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and that the systems described herein is merely exemplary embodiments of the present disclosure. 
     For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure. 
     With reference to  FIG. 1 , a monitoring system shown generally at  100  is associated with a vehicle  10  in accordance with various embodiments. In general, the monitoring system  100  monitors a controller area network (CAN) bus  12  of the vehicle  10  for faults. In various embodiments, the monitoring system monitors the CAN bus  12  using both a hardware-based approach and a software-based approach to monitor fault data, performs an analysis of the fault data, and generates a fault probability. The fault probability is then used to diagnose the CAN bus. 
     As depicted in  FIG. 1 , the vehicle  10  generally includes a plurality of controllers  14   a - 14   n  that communicate over the CAN bus  12 . Each of the controllers  14   a - 14   n  includes at least one processor  16   a - 16   n  and at least one computer readable storage device  18   a - 18   n . The processors  16   a - 16   n  can be any custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller  14   a - 14   n , a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer readable storage device or media  18   a - 18   n  may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor  44  is powered down. The computer-readable storage devices or media  18   a - 18   n  may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controllers  14   a - 14   n  in controlling the vehicle  10 . 
     The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processors  16   a - 16   n , receive and process signals from sensors (not shown), perform logic, calculations, methods and/or algorithms for controlling other components of the vehicle  10 , and generate control signals for controlling the components of the vehicle  10  based on the logic, calculations, methods, and/or algorithms. 
     In various embodiments, one or more instructions  102  of at least one of the controllers  14   a  are embodied in the monitoring system  100 , and when executed by the processor  16   a , monitor the CAN bus  12  for faults (e.g., link fault, controller fault, bus shorts, or impedance faults, CAN-H ground short, CAN-L ground short, CAN-H power short, CAN-L power short, CAN-L open, CAN-H open, etc.), generates a decision and a probability of the decision based on the monitoring, and diagnoses the CAN bus  12  based on the decision and the probability. In various embodiments, one or more of the instructions, when executed by the processor  16   a  further set a diagnostic code associated with the CAN bus  12  and/or generate a notification signal based on the monitoring. 
     Referring now to  FIG. 2 , a functional block diagram illustrates the instructions  102  of the monitoring system  100  grouped by modules in more detail. As can be appreciated, various embodiments of monitoring system  100  according to the present disclosure may include any number of sub-modules. As can further be appreciated, the sub-modules shown in  FIG. 2  may be combined and/or further partitioned to similarly monitor the CAN bus  12 . In various embodiments, the monitoring system includes  102  a software based solution module  30 , a hardware based solution module  32 , a propositions evaluation module  34 , and diagnosis module  36 . 
     The software based solution module  30  receives data  38  indicating faults on the CAN bus  12 . The data  38  includes CAN messages sent from each controller  14   a - 14   n  on the CAN bus  12 . For example, a message consists of 44 or more bits with a value of either 1 or 0. There is an identifier field in the CAN message indicating the message ID. Based on the vehicle signal dictionary, the message ID can be mapped to the sender controller name and acknowledged by the software based solution module  30 . Based on the received data  38 , the software based solution module  30  identifies a fault and generates a propositions set  40  based on software analysis methods. 
     For example, if a message from one controller  14   b  is regularly received, it indicates this controller  14   b  and the link between the controller  14   b  and the monitoring controller  14   a  are healthy. On the other hand, if a certain number of expected messages are missing, it indicates one or more faults may happen at the sender controller  14   b  or the CAN bus  12 . Therefore, by monitoring these messages, the software based solution module  30  can determine the activity of each controller  14   a - 14   n  and further make the diagnostic decision. 
     In various embodiments, the propositions set  40  is represented as f i,j  where i represents the fault type index (i=1, 2, . . . M) and j represents the location index (j=1, 2, . . . N) and a power set (Θ) is defined as the set of each signal fault and their combination (including all possible unions and uncertainty):
 
Θ={{ f   1,1   }, . . . ,{f   M,N   },{f   1,1   ,f   1,2   }, . . . ,{f   1,1   ,f   1,2   , . . . f   M,N }}.
 
     Similarly, the hardware based solution module  32  receives data  42  indicating faults on the CAN bus  12 . The data  42  includes some physical (electrical) measurements for CAN bus, e.g. CAN-H voltage, CAN-L voltage, bus current, bus resistance or bus capacitance. Based on the data  42 , the hardware based solution module  32  identifies a fault and generates a propositions set  44  based on hardware analysis methods. 
     For example, the pattern for physical measurements may be evaluated for an indication of different failure causes. For example, when a short fault between CAN-H and CAN-L happens, both CAN-H and CAN-L voltage are closed to each other and approximately equal to 2.5V and the bus resistance will be very small. Similarly, other faults including CAN-H short to ground, CAN-L short to ground, CAN-H short to power, CAN-L short to power, CAN-L open, CAN-H open, reversed wire connection or terminator loss have unique fault signatures in terms of the bus voltage or other physical measurements and can be evaluated. Therefore, by monitoring these physical measurements, the hardware based solution module  33  can determine a pattern and further make the diagnostic decision. 
     In various embodiments, the proposition set  44  is represented as f i,j  where i represents the fault type index (i=1, 2, . . . M) and j represents the location index (j=1, 2, . . . N) and a power set (Θ) is defined as the set of each signal fault and their combination (including all possible unions and uncertainty):
 
Θ={{ f   1,1   }, . . . ,{f   M,N   },{f   1,1   ,f   1,2   }, . . . ,{f   1,1   ,f   1,2   , . . . f   M,N }}.
 
     The propositions evaluation module  34  generates a decision  46  and a probability  48  associated with the decision based on the propositions set  40  from the software based solution module  30  and the propositions set  44  from the hardware based solution module  32 . As will be discussed in more detail with regard to  FIG. 3 , the propositions evaluation module  34  generates the decision  46  and the probability  48  based on the Dempster-Shafer evidence theory, decision-tree approach, a knowledge base, and/or other derivatives. The Dempster-Shafer theory is a mathematical approach to integrate different sources of information by considering the probability or confidence level of each source. The knowledge base is a heuristic approach to generate integrated decision from different sources of information using predefined rules. 
     The diagnosis module  36  diagnosis the CAN bus  12  based on the decision  46  and the probability  48  and generates notification signals and/or messages  50  based thereon. 
     Referring now to  FIG. 3 , and with continued reference to  FIGS. 1 and 2 , a flowchart illustrates a monitoring method  200  that may be performed by the monitoring system  100  in accordance with various embodiments. As can be appreciated in light of the disclosure, the order of operation within the method is not limited to the sequential execution as illustrated in  FIG. 3 , but may be performed in one or more varying orders as applicable and in accordance with the present disclosure. The method  200  may be varied e.g. the simplification of the method  200  or the decision tree approach, if the probability is not desired. This is because the decision tree approach is a special case of the Dempster-Shafer theory approach. 
     In one example, the method may begin at  205 . The fault data  38 ,  42  is loaded at  210 . The propositions set  44  from the hardware based solution module  32  is determined based on the fault data  42  at  220 ; and a propositions set  40  from the software based solution module  30  is determined based on the fault data  38  at  230 . 
     It is determined whether results for both hardware and software are available at  240 . For example, in some instances the hardware based solution or the software based solution provides no result when data  38  or data  42 , is not enough to make the decision. If results for both solutions are not available at  240 , the method continues with loading more data at  210 . 
     If, however, results for at least one of the solutions are available at  240 , the method continues with computing a probability mass of both propositions sets  40 ,  44  and generating a decision according to the Dempster-Shafer theory at  250 - 270 . 
     In particular, at  250 , the probability mass contributed by the proposition set  44  of the hardware based solution to the decision is determined as m H (f H ) using, for example, the following relation:
 
 m   H ( f   H )= f ( NH,SH ), m   H (Θ)=1− m   H ( f   H )  (1)
 
     and the probability mass contributed by the propositions set  40  of the software-based solution to the decision is determined as m S (f S ) using, for example, the following relation:
 
 m   S ( f   S )= g ( W   i   ,M   i ), m   S (Θ)=1− m   S ( f   S ).  (2)
 
     Where, NH represents the number of samples used in a hardware based solution. SH represents sampling rate used in the hardware based solution. W i  represents a length of a monitoring window of ECU i. M i  represents a message selected for monitoring ECU i&#39;s status. The probability mass functions (1) and (2) may be varied from the given form as long as they represent the probability or confidence level of hardware based solution or software based solution, respectively. 
     At  260 , the probability mass of the fault candidate m(f c ) is computed based on the probability mass contributed by the proposition set of the hardware-based solution m H (f H ) (substituted for m i (f i )) and the probability mass contributed by the proposition set of the hardware based solution m S (f S ) (substituted for m 2 (f 2 )) and using the following relations:
 
 m   c ( f   c )= m   1 ( f   1 )⊕ m   2 ( f   2 )= kΣ   f     1     ∩f     2     =f     c     m   1 ( f   1 ) m   2 ( f   2 ), where
 
 k   −1 =1−Σ f     1     ∩f     2     =Ø   m   1 ( f   1 ) m   2 ( f   2 ).  (3)
 
     At  270 , the decision of the final set of faults f I  is computed as: 
     
       
         
           
             
               
                 
                   
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     If however, f I  is equal to null set and the knowledge library is available at  280 , the final decision is recomputed using domain knowledge at  290 . For example, if the decision from the software-based approach is ECU normal or network normal, and the decision from the hardware-based approach is any fault, then the integration output will be the fault reported by hardware-based approach. This scenario may be caused by intermittent fault or the fault doesn&#39;t impact bus communication. 
     In another example, if the decision from the software-based approach is unknown fault and the decision from the hardware-based approach is ECU ground fault, then the integration output will be the ECU ground fault. This scenario may be caused by shared ground fault. In this situation, the list of inactive ECUs will be saved for further manual diagnosis. 
     In still another example, if the decision from the software based approach is unknown fault and the decision from the hardware based approach is bus voltage normal, both open or network normal, then the integration output will be the ECU or power fault. This scenario may be caused by shared power fault or ECU bubbling idiot fault (abnormally keep sending messages). In this situation, the list of inactive ECUs will be saved for further manual diagnosis. 
     Thereafter, if the decision is not equal to the previously determined decision at  300 , the method continues at  210  with loading more data  38 ,  42  and computing new propositions sets  40 ,  44 . If, however, the determined decision  46  is equal to the previously determined decision at  300 , then a counter C is incremented at  310  and evaluated at  320 . If the counter C has not reached a threshold T 1  (e.g., 3, 4, 5, or other number), or stated another way, the decision  46  has not been the same for a threshold number of iterations, the method continues with loading data at  210  and computing new propositions sets  40 ,  44 . 
     Once the counter has reached a threshold at  320 , a decision can be made. The fault set and the determined probability is saved and the CAN bus is diagnosed based on the saved data at  330 . Thereafter, the method may end at  340 . 
     In various embodiments, the propositions sets  40 ,  44  from the hardware based solution module  32  and the software based solution module  30  may be compatible. For example, assume there is a hardware fault, such as, CAN-H open fault on link  1  (between a first controller and a second controller) indicated as f 7,1 . The 7-th knowledge source in the hardware based solution module  32  is considered as the output. Thus, the propositions set  44  from the hardware based solution module  32  is {(f 7,1  ∪ f 7,2  . . . ∪ f 7,13 )}, and the propositions set  40  from software based solution module  30  is {(f 6,1  ∪ f 7,1  ∪ f 10,1  ∪ f 9,14  ∪ f 10,14 )}. Exemplary probability masses for network status identification contributed by the hardware based solution module  32  and the software based solution module  30  are computed by the propositions evaluation module  34  as: 
     
       
         
           
             
               
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                 TABLE 1 
               
               
                   
                   
               
               
                   
                 m s (f s ) = 0.98 
                 m s (Θ) = 0.02 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 m h (f h ) = 0.92 
                 m(f h  ∩ f s ) = m(f 7,1 ) = 0.9016 
                 m(f h  ∩ Θ) = m(f h ) = 
               
               
                   
                   
                 0.0184 
               
               
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                 m(Θ ∩ f s ) = m(f s ) = 0.0784 
                 m(Θ) = 0.0016 
               
               
                   
               
            
           
         
       
     
     Where f h  ∩ f s  represents the proposition formed by the intersection of (f 71  ∪ f 7,2  . . . ∪ f 7,13 ) from the hardware based solution module and (f 6,1  ∪ f 7,1  ∪ f 10,1  ∪ f 9,14  ∪ f 10,14 ) from the software based solution module. f h  ∩Θ represents the proposition formed by the intersection of (f 7,1  ∪ f 7,2  ∪ f 7,13 ) from the hardware based solution module and the uncertainty (Θ) from the software based solution module. Θ represents the intersection of the uncertainty propositions from the hardware based solution module and the software based solution module. The proposition represented by m h (f 7,1 ) has the highest probability mass. Thus, the propositions evaluation module  34  selects m h (f 7,1 ) as the output to represent the fusion of the evidence from the hardware based solution module  32  and the software based solution module  30  and the final decision  46  and probability  48 . 
     In various embodiments, the propositions sets  44 ,  40  from the hardware based solution module  32  and the software based solution module  30  may not be compatible. For example, assume there is an ECU (RDCM) floating fault that occurred which is indicated as f 9,20 . The inter-frame voltage offset due to this fault is not significant and the fault itself might not be detected by the hardware based solution module  32 . As a result, the 24-th knowledge source in the hardware based solution module  32  is considered as the output. Thus, the propositions set  44  from the hardware based solution module is {(f 13,1  ∪ f 13,2 , . . . ∪ f 13,13  ∪ f 11  ∪ f 12 )}, and the propositions set  40  from the software based solution module  30  is {f 9,20 }. The probability masses for the network status identification contributed by the hardware based solution module  32  and the software based solution module  30  are computed by the propositions evaluation module  34  as: 
     
       
         
           
             
               
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               . 
             
           
         
       
     
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 m s (f s ) = 0.98 
                 m s (Θ) = 0.02 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 m h (f h ) = 0.90 
                 m(f h  ∩ f s ) = m(Ø) = 0.882 
                 m(f h  ∩ Θ) = m(f h ) = 
               
               
                   
                   
                 0.018 
               
               
                 m h (Θ) = 0.10 
                 m(Θ ∩ f s ) = m(f s ) = 0.098 
                 m(Θ) = 0.002 
               
               
                   
               
            
           
         
       
     
     k −1 =1−0.882=0.118, k=8.475 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 m s (f s ) = 0.98 
                 m s (Θ) = 0.02 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 m h (f h ) = 0.90 
                 m(f h  ∩ f s ) = m(Ø) = 0 
                 m(f h  ∩ Θ) = m(f h ) = 
               
               
                   
                   
                 0.1525 
               
               
                 m h (Θ) = 0.10 
                 m(Θ ∩ f s ) = m(f s ) = 0.83055 
                 m(Θ) = 0.01695 
               
               
                   
               
            
           
         
       
     
     The proposition represented by m s (f 9,20 ) has the highest probability mass. Thus, Thus, the propositions evaluation module  34  selects m s (f 9,20 ) as the output to represent the fusion of the evidence from the hardware based solution module  32  and the software based solution module  30  and the final decision  46  and probability  48 . 
     While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.