Abstract:
A model-based Fault Detection and Isolation (FDI) method and system for monitoring the overall performance in a vehicle system based on a hierarchical structure is disclosed. The FDI scheme uses the available sensors in a vehicle system and divides them into subsystems of smaller dimensions containing one or more modules that are related or interconnected. The same module may appear in a different subsystem, but the set of all subsystems does not have to contain all of the modules. For this structure, an FDI scheme comprising several detector units is created. Each detector unit receives information from the sensors and outputs a residual that is sent to a high-level detector unit which processes the data and performs the residual evaluation for the selected subsystem. Finally, each subsystem outputs a decision that is sent to a supervisor hazard detector performing the final diagnosis.

Description:
This application claims the benefit of U.S. Provisional Patent Application No. 60/247,849 entitled FAULT DETECTION AND ISOLATION SYSTEM AND METHOD and filed Nov. 9, 2000. 
    
    
     This application claims the benefit of U.S. Provisional Patent Application No. 60/247, 849, entitled FAULT DETECTION AND ISOLATION SYSTEM AND METHOD and filed Nov. 9, 2000. 
     TECHNICAL FIELD 
     The present invention is in the field of vehicle control system design. More particularly, the present invention is a model-based fault detection and fault diagnosis system and method for monitoring overall vehicle system performance. 
     BACKGROUND AND SUMMARY OF THE INVENTION 
     In recent years, increasing interest and requirement for improved vehicle performance, reliability, and safety has focused attention on the use of Fault Detection &amp; Isolation (FDI) when designing vehicle control systems. Fault detection and isolation is becoming one of the most important aspects in vehicle system control design. In order to meet the increasing demand for better performance and reliability, model-based FDI schemes are being developed to address complete vehicle systems, to detect faults in sensors and actuators, and to apply appropriate corrective action without adding new hardware to the vehicle. However, the high complexity of most vehicle systems makes the standard FDI model-based technique difficult to apply without unacceptable computational effort. 
     The present invention is a novel system and method based on a hierarchical structure of the FDI scheme that reduces the computational effort of prior art systems. The FDI scheme uses the available sensors in a vehicle system and divides them into subsystems of smaller dimensions containing one or more modules that are related or interconnected. The same module may appear in a different subsystem, but the set of all subsystems does not have to contain all of the modules. For this structure, an FDI scheme comprising several detector units is created. Each detector unit receives information from the sensors and outputs a residual that is sent to a high-level detector unit which processes the data and performs the residual evaluation for the selected subsystem. Finally, each subsystem outputs a decision that is sent to a supervisor hazard detector performing the final diagnosis. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a block diagram of a vehicle model for an example embodiment of the present invention; 
     FIG. 2 is a block diagram of the general structure of a model-based FDI method; 
     FIG. 3 is a block diagram of a residual generator in accordance with an example embodiment of the present invention; 
     FIG. 4 is a block diagram of a hierarchical diagnostic system in accordance with an example embodiment of the present invention; 
     FIG. 5 is a block diagram for the structure of a detector unit in accordance with an example embodiment of the present invention; 
     FIG. 6 is a block diagram of a general module in accordance with an example embodiment of the present invention; 
     FIG. 7 is a block diagram of a residual evaluation unit in accordance with an example embodiment of the present invention; 
     FIG. 8 is a block diagram of the FDI scheme of the present invention; 
     FIG. 9 is a graph of steering wheel angle input; 
     FIGS. 10-12 are graphs of estimated and actual state variables; and 
     FIGS. 13-18 are graphs of experimental results for a J-turn. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention may be implemented in accordance with software components that provide the features and functionality described herein. Referring to FIG. 1, a vehicle may be represented, in general, as a block diagram as shown in FIG. 1 constituting of two main subsystems: the core subsystem  124  and the external subsystem. The core subsystem  124  comprises the vehicle  114 , tire  120 , powertrain  118 , steering  112 , suspension  116 , and brake  108  modules. The vehicle module  114  comprises a 16DOF vehicle model. The vehicle model further comprises a vehicle body, (i.e., the sprung mass), and four wheels, (i.e., the unsprung masses). The model contains three translational degrees of freedom—longitudinal, lateral, vertical, and three rotational degrees of freedom—roll, pitch, and yaw for the sprung mass. Each of the unsprung masses has vertical, spin, and steering angle degrees of freedom. The tire module  120  has as inputs the longitudinal slip, the lateral slip, the vertical load, and the camber angle which gives as output the longitudinal and lateral force as well as aligning moment. The powertrain module  118  comprises the engine, the transmission, and the differential models. The engine uses a lookup table with throttle position and engine speed as inputs and gives as output the engine torque. The transmission model inputs the engine torque and transforms the torque based on the selected gear. The differential model proportions the torque from the transmission to the drive wheels. The steering module  112  describes the elastic and geometric properties of the steering system. The suspension module  116  comprises the model of the suspension that may be of four different types: linear spring and damper, nonlinear spring and damper, semiactive suspension, and active suspension. The brake module  108  generates the wheel torques as a function of the driver brake pedal force and brake controller commands. 
     The external subsystem comprises the environmental module  122 , driver module  110 , sensor module  100 , brake controller module  102 , suspension controller module  104 , and communication module  106 . The environmental module  122  determines the interfaces between the vehicle and the environment. The driver module  110  determines the interface between the driver and the vehicle. This module provides information such as brake pedal force, steering angle, throttle position, and desired gear to the core module. The sensor module  100  models the sensor dynamics. The outputs of this module are sent to the controller module. The brake controller  102  and suspension controller  104  contain algorithms used to control the brake, and the suspension systems. The communication module  106  models communication delays which occur in communication links between controllers. 
     In the model-based FDI system and method of the present invention, analytical redundancy is used rather than physical redundancy. This analytical redundancy is contained in the static and dynamic relationship between the input and the output variables of the system. The sensitivity of a diagnostic method to modeling error is one of the key issues in the application of model-based FDI methods. In most cases, model-based FDI methods can be described by the block diagram shown in FIG.  2 . 
     When an accurate model of the plant is available, the general process of the model-based FDI consists of the three stages depicted in FIG.  3 . At the first stage, observations  160  acquired through sensor measurements are compared to analytical values of the same variables in a primary residual generator  162 . The error between measured and calculated variable is called a primary residual. This residual reflects the system behavior, and has nominal zero mean value under normal conditions. At the second stage, the primary residuals that usually deviate from zero due to noise, modeling error and faults, are communicated to a secondary residual generator  164  and converted in secondary residuals by means of filtering, statistical testing ,or spectral analysis to obtain signals that can be used to analyze and isolate faults. Finally, the secondary residuals are communicated to a decision maker  166  and analyzed to isolate the fault and a diagnosis  168  or decision is taken. 
     In accordance with the present invention, the vehicle system is decomposed into subsystems of smaller dimension containing one or more modules strictly related or interconnected. Referring to FIG. 4, for this structure, the FDI scheme comprises a plurality of fault detector units  186 ,  188 ,  192 ,  194 ,  198 ,  200 . Each fault detector unit  186 ,  188 ,  192 ,  194 ,  198 ,  200  outputs a residual that is sent to a residual evaluation unit  184 ,  190 ,  196  that performs the residual evaluation for the selected subsystem. Finally, each subsystem  184 ,  190 ,  196  outputs a decision that is sent to a supervisor fault detector  182  performing the final diagnosis  180 . As shown in FIG. 4, some different subsystems for the vehicle are shown and each is constituted by a residual evaluation unit  184 ,  190 ,  196  and a plurality of fault detector units  186 ,  188 ,  192 ,  194 ,  198 ,  200 . The scheme for a fault detector unit  222  is depicted in FIG.  5 . 
     In general, a module may be represented as in FIG. 6 where: 
     u 0l, i= 1 . . . m are the input vectors 
     Δu i , i=1 . . . m are the input fault vectors 
     θ 0l , i=1 . . . m are the nominal parameter vectors 
     Δθ l , l=1 . . . m are the parameter fault vectors 
     x i , i=1 . . . m are the state vectors 
     Δy is the output fault vector 
     y is the output measured vector. 
     The module can be described by the following equations                      {                 x   .     1     =       f   1          (       x   1     ,     u   1     ,     θ   1       )                   y   =         h   1          (       x   1     ,     u   1     ,     θ   1       )       +     Δ                 y               ,               x   1     ∈     Γ   1               ⋮       ⋮             {                 x   .     m     =       f   m          (       x   m     ,     u   m     ,     θ   m       )                   y   =         h   m          (       x   m     ,     u   m     ,     θ   m       )       +     Δ                 y               ,               x   m     ∈     Γ   m                        (1)                                
     with u 0l ,=u 0l +Δu i , θ i =θ 0l +Δθ i , i=1 . . . m, and where Γ i  is a subset in which the i th model equations are valid. A fault detection unit is associated with each module. Each fault detection unit contains a multimodel representation of the type                      {                   x   ^     .     1     =       g   1          (         x   ^     1     ,     u   1     ,       θ   ^     1     ,   y     )                       y   ^     1     =       h   1          (         x   ^     1     ,     u   1     ,       θ   ^     1       )               ,                 x   ^     1     ∈     Γ   1               ⋮       ⋮             {                   x   ^     .     m     =       g   m          (       x   m     ,     u   m     ,       θ   ^     m     ,   y     )                       y   ^     m     =       h   m          (         x   ^     m     ,     u   m     ,       θ   ^     m       )               ,                 x   ^     m     ∈     Γ   m                        (2)                                
     characterized by the fact that, without any fault, the following conditions hold 
     
       
           {circumflex over (x)}   i   →x   i  for  t→∞, i =1  . . . n   (3) 
       
     
     The primary residuals  230 ,  240 ,  246  are sent to the high-level fault detector decision unit  236  as shown in FIG.  7 . In this decision unit  236 , the residual evaluations for the subsystem are performed at the residual evaluation units  234 ,  244 ,  250  and the result from the decision unit  236  is sent as input to the supervisor fault detector  238 . 
     The method of the FDI scheme of the present invention comprises the following steps: 
     1. Partition of the vehicle model into subsystems containing one or more interconnected modules. The same module may appear in more then one subsystem, but the set of all subsystems, in general, does not have to contain all the modules. 
     2. Associate a fault detector unit to each module or smaller partition and define a multimodel representation and selection of a residual generation method for every subsystem. The method for residual generation may be of different type, but commonly used approaches are the parity space method, the observer method, and the parameter identification method. 
     3. Define an appropriate residual evaluation method for each subsystem. 
     To illustrate the method for a specific case, consider the subproblem of fault detection for three important sensors: 
     the lateral acceleration sensor; 
     the steering wheel angle sensor; 
     the yaw rate sensor; and for two parameters: 
     the front cornering stiffness; and 
     the rear cornering stiffness. 
     The structure of this example FDI scheme is shown in FIG.  8 . 
     The FDI scheme shows a fault detection unit where only one multimodel representation for a simplified front wheel steered, small angle, bicycle model structure is considered. The dependence of the vehicle lateral velocity and yaw rate and the longitudinal velocity on the steering input is modeled. A simplified tire force model is adopted, whereby the lateral forces of the front and rear tires are linearly related to the front and rear slip angles, through Cf and Cr the front and rear cornering stiffness. The model is valid for nonsevere maneuvers, (i.e., for a lat ≧0.2 g, where g is the acceleration due to gravity). The nonlinear model can be described by the equations              {                 v   .     x     =         F   x     M     +       v   y          Ψ   .                         v   .     y     =         -     2   M            (       C   f     +     C   r       )            v   y       v   x         -       2   M          (       aC   f     -     bC   r       )            Ψ   .       v   x         -       v   x          Ψ   .       +         2                   C   f       MG                   δ                     Ψ   ¨     =         -     2   I                       (       aC   f     -     bC   r       )                       v   y       v   x         -       2   I          (         a   2          C   f       +       b   2          C   r         )                       Ψ   .       v   x         +         2        aC   f       IG                   δ                    
              a         is                 the                 distance                 from                 front                 wheel                 to                   C   .              G   .              of                   the                 vehicle             b         is                 the                 distance                 from                 rear                 wheel                 to                   C   .              G   .              of                   the                 vehicle               C   f           is                 the                 front                 cornering                 stiffness               C   r             is                 the                 rear                 cornering                 stiffness                          M         is                 the                 vehicle                 mass             I         is                 the                 vehicle                 moment                 of                 inertia             G         is                 the                 gear                 ratio               F   x           is                 the                 longitudinal                 force               v   x           is                 the                 vehicle                 longitudinal                 velocity               v   y             is                 the                 vehicle                 lateral                 velocity                          δ         is                 the                 steering                 angle               Ψ   .           is                 the                 yaw                 rate                     (4)                                
     For this model, it is possible to design the following sliding mode nonlinear observer based only on the yaw rate measurement                  x   ^     .     =           (       ∂     H        (     x   ^     )           ∂     x   ^         )       -   1            M        (     x   ^     )            sign        (       V        (   t   )       -     H        (     x   ^     )         )         +     B                 δ               (5)                                
     where                H        (   x   )       =                [         h   1          (   x   )              h   2          (   x   )              h   3          (   x   )         ]                     h   1          (   x   )       =       Ψ   .                =   r                     h   2          (   x   )       =                r   .                     h   3          (   x   )       =                r   ¨                   V        (   t   )       =                [         v   1          (   t   )              v   2          (   t   )              v   3          (   t   )         ]                     v   1          (   t   )       =                r        (   t   )                     v     i   +   1       =                (           m   i          (     (     x   ^     )     )              sign        (     x        (         v   i          (   t   )       -       h   i          (       x   ^          (   t   )       )         )       )       eq       ,                i   =   1     ,   2                     M        (     x   ^     )       =                diag        (         m   1          (     x   ^     )              m   2          (     x   ^     )              m   3          (     x   ^     )         )                                    
     The following table shows the error signatures. 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Error Signature 
               
             
          
           
               
                 no. 
                 fault variable 
                 cause 
                 resid. pattern 
               
               
                   
               
               
                 1 
                 wheel steering angle δ 
                 actuator failure 
                 [1 0 1 0 1 1 1] 
               
               
                 2 
                 lateral accel a lat   
                 sensor failure 
                 [1 0 1 0 1 0] 
               
               
                 3 
                 yaw rate r 
                 sensor failure 
                 [1 1 1 1 1 1] 
               
               
                 4 
                 Cf front cornering 
                 blow out/incorrect inflat. 
                 [0 1 0 1 1 1] 
               
               
                   
                 stiffness 
               
               
                 5 
                 Cr rear cornering 
                 blow out/incorrect inflat. 
                 [1 1 1 1 0 1] 
               
               
                   
                 stiffness 
               
               
                   
               
             
          
         
       
     
     To simplify the problem, consider only the case of single faults. The residual vector is 
       R={a   lat   −â   y1   δ−{circumflex over (δ)}a   lat   −â   y2   C   f   −C   f   a   lat   −â   y3   C   r   −Ĉ   r }  (6) 
     With the choice made above, the error signature described in the Table 1 may be derived. 
     Some simulation and experimental results obtained from the previous FDI scheme using sliding mode observers illustrate the system and method of the present invention. The tests are carried out for a vehicle with the parameter data set as in table 2. 
     
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Parameter Values Utilized in the Steering Model. 
               
             
          
           
               
                   
                 parameter 
                 value 
                   
               
               
                   
                   
               
             
          
           
               
                   
                 a 
                 1.0 
                 [m] 
               
               
                   
                 b 
                 1.69 
                 [m] 
               
               
                   
                 Cf 
                 60530 
                 [N/rad] 
               
               
                   
                 Cr 
                 64656 
                 [N/rad] 
               
               
                   
                 M 
                 1651 
                 [Kg] 
               
               
                   
                 I 
                 2755 
                 [Kg/m2] 
               
               
                   
                 G 
                 1 
                   
               
               
                   
                 Fx 
                 100 
                 [N] 
               
               
                   
                   
               
             
          
         
       
     
     Referring to FIG. 9, the steering input for a vehicle lane change maneuver at a longitudinal velocity of 25 mph (11 m/s) and without any fault is shown. 
     The relative state variable estimations (dashed line) are represented in FIGS. 10-12. It is possible to notice that, after a fast transient, the estimates track the true variable with a very small error. 
     In FIGS. 13-18, the experimental results for a Jturn at constant forward velocity and step change in the steering angle are presented. A steering input fault of 1.25 times the commanded input has been applied during the test. FIGS. 13 and 14 show the residuals for lateral acceleration and steering angle respectively obtained from Unit A 1 . In dashed line are indicated the estimate values from the observer, a flag  0  (threshold evaluation) may be associated to the lateral acceleration residual while a flag  1  is associated to steering angle residual. 
     The residuals for lateral acceleration and front tire cornering stiffness obtained from Unit A 2  are depicted in FIGS. 15 and 16 while in FIGS. 17 and 18 the lateral acceleration and the rear cornering stiffness are compared with the measured values. At the end, the following residual signature is observed 
     
       
           R ={0 1 0 1 1 1}  (7) 
       
     
     which indicates a steering inputs or C f  fault. 
     The present invention supports implementation of a vehicle health monitor to increase the reliability of a passenger vehicle with experimental validation of the observer design and FDI scheme. While particular embodiments of the invention have been illustrated and described, various modifications and combinations can be made without departing from the spirit and scope of the invention, and all such modifications, combinations, and equivalents are intended to be covered and claimed.