Abstract:
Disclosed is a light rail vehicle having a predictive diagnostic system for a motor driven automated door ( 100 ) to enable condition-based maintenance. The light rail vehicle ( 110 ) has an automated door system ( 112 ), at least one data acquisition board ( 114 ), a data collection program ( 116 ), an exponential smoothing algorithm ( 118 ), and a neural network ( 120 ). The need for maintenance is identified through the collection of various door system ( 112 ) parameters, calculating current energy and time consumption from these parameters, and determining the rate of degradation based on current energy and time consumption of the door system ( 112 ) as compared with historical energy and time consumption. From the rate of degradation, maintenance can be scheduled as needed.

Description:
This application claims benefit of provisional application 60/163,845 filed Nov. 5, 1999. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention is directed to a light rail vehicle with a predictive diagnostic system for a motor driven automated door system. The diagnostic system monitors the automated door system to enable condition-based maintenance. 
     2. Description of the Prior Art 
     A motor driven automated door system of a light rail vehicle is an electromechanical system that degrades over time. Currently, the automated door system requires labor-intensive preventative maintenance in order to ensure high reliability. This “time-based” maintenance approach results in the system and its components being maintained even when there is no need for maintenance, resulting in high maintenance costs, ineffective use of maintenance resources, and significant downtime. 
     SUMMARY OF THE INVENTION 
     It is an object of this invention to provide an alternative to scheduled preventative maintenance of a light rail vehicle automated door system that will reduce maintenance costs and increase maintenance efficiency while maintaining or increasing the availability of the automated door system. 
     Accordingly, we have developed a predictive diagnostic system for use with a light rail vehicle automated door system to monitor the door system and identify when maintenance is necessary based on measured door system conditions. The predictive diagnostic aspect of the present invention requires knowing normal and failure door system conditions, determining current state door characteristic deviations, calculating rate towards failure conditions, and invoking maintenance before failure conditions occur. 
     As the automated door system operates, weather conditions, foreign substances in the path of the doors or in the door tracks, normal wear of components through friction and stress, people holding the doors open, etc. cause degradation of system components, such as the electrical motor, levers, rollers, and/or tracks. This degradation leads to failures, such as improper opening and closing of the door, worn out rollers, a bent operator arm, and a worn out operator arm track. These failures increase the frictional resistance against the door, causing the motor to work harder. Therefore, the effect of friction on the door is an important diagnostic parameter for the automated door system. 
     The predictive diagnostic system for the automated door system of a light rail vehicle according to the present invention monitors the motor driven door system and determines its current health and rate of degradation. During operation of the door system, several parameters are checked for characteristic deviations that indicate an impending system failure. Deviations are detected by comparing the current state of the parameters to past parameter values, whereby the current health and the rate of degradation of the door system are determined. From the rate of degradation and a known point where the system requires maintenance, a predicted time to failure can be determined. In turn, an indication of the required maintenance can be provided, thereby avoiding unnecessary preventative inspection of healthy equipment. 
     The diagnostic system, therefore, is able to predict door system failures far enough in advance so that the required maintenance can be performed during scheduled maintenance periods. The benefit of using predictive diagnostics is that the automated door system will not be over-maintained, but rather maintained only when necessary, resulting in a reduction in degradation-type failures, a reduction in maintenance costs, an increase in maintenance efficiency, and an increase in system availability. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a schematic view of the present invention; 
     FIG. 2 is a front plan view of the automated door system of FIG. 1; and 
     FIG. 3 is a flow diagram illustrating the general process according to the present invention. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     A complete understanding of the invention will be obtained from the following description when taken in connection with the accompanying drawing figures wherein like reference characters identify like parts throughout. 
     FIG. 1 illustrates a light rail vehicle with a predictive diagnostic system for a motor driven automated door  100  according to the present invention. The present invention includes a light rail vehicle  110 , an automated door system  112 , at least one data acquisition board  114 , a data collection program  116 , an exponential smoothing algorithm  118 , and a neural network  120  (the data collection program  116 , the exponential smoothing algorithm  118 , and the neural network  120  are computer based programs). 
     A neural network is a computer model capable of drawing conclusions from a set of conditions. (See Fausett, L.,  Fundamentals of Neural Networks , Prentice Hall (1994); Haykin, S.,  Neural Networks—A Comprehensive Foundation , Prentice Hall (1994).) Neural networks are trained using observations collected from the system under investigation. Once trained, the neural network recognizes patterns similar to those it was trained on and classifies the new patterns accordingly. The neural network of the present invention is used as a state assessment tool. Thus, prior to use in the predictive diagnostic system for a light rail vehicle automated door system  100 , the neural network, has to learn various system conditions by comparing models of the observed system, the system in normal operation (i.e., operating to specifications), and the system running into failure. 
     For example, data may be collected for training the neural network by setting up a test door system to simulate the door system in operation. Data may be collected while the system is operated under normal conditions (i.e., running to specifications). Friction may be applied to the system to simulate the door system running with degradations in the system. Different amounts of friction may be applied to simulate different stages or types of degradation. Known failed parts may be installed to simulate the system running under failure conditions. The data collected at the various states of system operation may then be fed to the neural network for training purposes. 
     FIG. 2 illustrates a light rail vehicle automated door system  112 . Typically, the light rail vehicle  110  has four pairs of doors  212 , with each door  212  controlled by a dedicated door system  112 . Each door system  112  typically has a motor  210 , a door  212 , an operator arm  214 , an upper track  216 , a lower track  218 , and a plurality of switches  226  fixedly positioned around a cam  224  that is coupled to a shaft  225  of the motor  210 . Each door  212  also has an operator arm track  220  and rollers  222 . The switches  226  are utilized to monitor a position of the door  212  as a function of a lobe of the cam  224  engaging each switch in response to rotation of the cam  224  when the motor  210  opens and/or closes the door  212 . Alternatively, the door system  112  may be any conventional door system used with light rail vehicles. For example, the door system  112  may use encoders (not shown) instead of a cam  224  and switches  226  to determine the position of the door  212 . As another example, the door system  112  may be a sympathetic drive system with a screw drive (not shown) that drives both doors  212  of a pair of doors or with a pair of screw drives (not shown) for driving both doors  212 . 
     In use with the above-described typical configuration, each automated door system  112  receives signals from a control system which instruct the motor  210  to open or close the door  212 . The motor  210  turns the cam  224  and the operator arm  214 . The operator arm track  220  guides the movement of the operator arm  214 . The movement of the operator arm  214  pulls or pushes the door  212  to an open or closed position. The top of the door  212  has rollers  222  that ride in the upper track  216  for guidance. The bottom of the door  212  is guided by the lower track  218 . As the door  212  goes through its cycle, a lobe of the cam  224  selectively causes each switch  226  to activate and connect or disconnect resistors in an electrical circuitry of the system  112  to change the speed of the moving door  212 , as well as other door control functions. Activation of the switches  226  also indicates the position of the door  212  through its travel. Again, any conventional door system or system that indicates the position of the door  212  through its open and close cycle may be utilized. 
     Referring to FIGS. 1 and 2, the light rail vehicle predictive diagnostic system  100  monitors the motor driven door system  112  and determines its current health and rate of degradation. As the door cycles open and closed, the motor  210  turns the cam  224  and the operator arm  214 . The switches  226  send signals to the data acquisition board  114  based on the position of the cam  224 . These signals indicate the position of the door  212  through the cycle. Motor  210  current and voltage are also sent to the data acquisition board  114 . Preferably, the data acquisition board  114  collects  100  samples per second. However, other sampling rates may be used. 
     Data collected by the data acquisition board  114  is processed by the data collection program  116 . The data collection program  116  may be integral with the data acquisition board  114  or may execute in a computer, for example, a laptop personal computer. The data acquisition board  114  may send the data to the data collection program  116  on a computer via an RS485 serial network, although other networks can be used. The computer may house the data acquisition board  114  and the data collection program  116 . The data collection program  116  may be written in the C++ language. The data collection program  116  may be designed to collect data when both the closing voltage and the opening voltage are not equal to zero (i.e., collecting data only when the door system  112  is operated). Data may be stored in any suitable format for the diagnostic system  100 . As an example, data may be stored such that the first eight data bits are digital I/O and the remaining bits are voltage on an armature of the motor  210  during an open operation, voltage on the armature of the motor  210  during a close operation, three current measurements, temperature, and frame number (i.e., record number). 
     The data collection program  116  calculates energy and time consumption for the door system through its cycle. Current and historical consumption values are provided to the exponential smoothing algorithm  118  for processing into an input set to be submitted to the neural network  120 . Preferably, the exponential smoothing algorithm  118  and the neural network  120  are housed on the same computer as the data collection program  116 . Alternatively, more than one computer may be utilized for the data collection program  116 , the exponential smoothing algorithm  118 , and the neural network  120 . Where more than one computer is utilized, the computers may be able to communicate with each other. 
     The neural network  120  determines the rate of degradation. From the rate of degradation and a known point where the door system  112  requires maintenance, a predicted time to failure can be determined. Maintenance is scheduled based on the rate of degradation. The particular benefit of predictive diagnosis is the ability to anticipate failure before it actually happens. 
     The neural network of the present invention can be one of any conventional neural network paradigms. The preferred embodiment of the present invention utilizes one of the following neural networks: backpropagation, cascade correlation network, or radial basis function. These three neural networks are supervised networks that serve as universal function approximators. 
     FIG. 3 illustrates the general process  300  of the predictive diagnostic system for the light rail vehicle motor driven automated door  100 . The data acquisition board  114  is used in step  310  to collect data from the automated door system  112 . The data collected includes motor current motor voltage, and switch signals through discrete positions of door travel. The data acquisition board  114  sends the data to the data collection program  116 . The current used by the door motor is used to determine current through the motor by subtracting the current through either an open or closed resistor circuit from the total current. The armature voltage data is used in energy and door power calculations. Data from the door switches is used for timing information and door status. 
     In step  312 , the data collection program  116  calculates current energy and time consumption values for the system. For the light rail vehicle automated door system  112  of the present invention, energy is calculated as:        I   =         c   3          V   sg           c   4          c   5                                
     where I denotes the current, V C  and V D  represent closing voltage and opening voltage, respectively, V sg  denotes the voltage shunt to the ground, and T denotes the time between two samples. c 1 , c 2 , c 3 , c 4 , and c 5  are conversion factors selected for the given circuit in the preferred embodiment where five switches are used to measure door position. The data collection program  116  sends both current and historical energy and time consumption values to the statistical smoothing algorithm  118  in steps  314  and  316 , respectively. 
     In step  318 , the exponential smoothing algorithm  118  produces a set of energy and time consumption values based on the current and historical energy and time consumption data. The exponential smoothing algorithm  118  reduces noise in the data and detects the trend of the degradation. The exponential smoothing algorithm  118  is a well-known algorithm customized for use in the present invention as follows: 
     
       
         
               
             
               
               
               
             
           
               
                   
               
               
                 O-S-A (One-Step-Ahead) Forecast F t  = S t−1  + G t−1   
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Mean S t   
                 = αD t  + (1 − α)(S t−1  + G t−1 ) 
               
               
                   
                   
                 = αD t  + (1 − α)F t   
               
               
                   
                 Trend G t   
                 = β(S t  − S t−1 ) − (1 − β)G t−1   
               
               
                   
                   
               
             
          
         
       
     
     where D t  represents the original data and α and β denote smoothing constants which are chosen based on the system. In order to compare the effect of various smoothing constants, a Mean of Absolute Forecast Error (MAFE) may be calculated for sets of α and β and the set of α and β having the smallest MAFE may be selected for the system. 
     The set of energy and time consumption values are submitted as input to the neural network  120 . In step  320 , the neural network  120  generates a degree of degradation of the system (conclusion) based on the set of energy and time consumption values (conditions). Degradation may be reported in terms of a confidence of a degradation prediction, an estimated severity, an estimated time to failure, and a cause of degradation ranked by likelihood. From the degree of degradation and a known point where maintenance is required (a threshold level), a predicted time to failure can be determined. The threshold level may be initially defined by maintenance personnel based on experience or manufacturer requirements. The threshold level may be adjusted as necessary. In steps  322 ,  324 , and  326 , if the degree of degradation exceeds the threshold level, for example, greater than 0.5, maintenance is recommended; otherwise, the process  300  repeats itself starting with the collection of data in step  310 . In step  324 , the system  112  or its components may be recommended for maintenance attention immediately or at a particular point in the future, for example, after a specific number of open and close cycles. 
     It will be understood by those skilled in the art that while the foregoing description sets forth in detail preferred embodiments of the present invention, modifications, additions, and changes might be made thereto without departing from the spirit and scope of the invention.