Data processing: measuring – calibrating – or testing – Measurement system – Performance or efficiency evaluation
Reexamination Certificate
1999-09-30
2002-08-13
Hoff, Marc S. (Department: 2853)
Data processing: measuring, calibrating, or testing
Measurement system
Performance or efficiency evaluation
C714S798000
Reexamination Certificate
active
06434512
ABSTRACT:
BACKGROUND OF THE INVENTION
a. Field of the Invention
The present invention relates generally to a data collection and analysis system for facilitating intelligent diagnostics and failure prevention.
b. Description of the Related Art
Dynamic systems such as transportation-systems and in particular vehicles are widely employed. In commercial settings, vehicles such as automobiles, trucks, buses, etc. are relied upon to operate with minimum maintenance over extended periods of time. For many businesses, a fleet of vehicles must be maintained and their usage coordinated for safe and efficient utilization of vehicle resources. For these businesses, downtime, and the random nature of it, is a significant concern. Likewise, passenger vehicles are also widely employed and, for consumers, these vehicles must be maintained to ensure driver and passenger safety.
Notably, such vehicles typically have a plurality of costly subsystems. In most vehicles, at least a small percentage of vehicle subsystems including lubrication, brakes, etc., are prone to failure at any time and, therefore, periodically require maintenance. Typically, maintenance is performed on a schedule (e.g., change the oil every 3,000 miles) without regard to the actual health status of the associated subsystem.
In addition to normal aging and use, failures due to poor or no maintenance and improper operation of the vehicle (e.g., “riding” the brakes, aggressive driving, etc.) take a significant toll on the vehicles, thus resulting in significant expense and logistical problems for the owner. Moreover, this problem is exacerbated when maintaining a large number of vehicles. As a result, a convenient yet inexpensive failure prediction system is desired.
Depending on the application, the failure of a vehicle in service can lead to overall system or process downtime, inconvenience, and potentially, the creation of a hazardous situation. Thus, it is desirable to monitor and diagnose potential failure or faults in these vehicle subsystems early in order to avoid such problems. Absent intelligent monitoring of the subsystems, these problems can have an insidious effect when a problem, although relatively minor and correctable if detected early, goes undetected and yields a set of circumstances that becomes more serious the longer the problem goes undetected. For example, steering and more general torque problems may not become apparent until irreversible damage has occurred. Likewise, problems due to inadequate lubrication, contamination or a multitude of other causes may not become apparent until the subsystems are damaged or a catastrophe occurs.
In order to reduce the probability of failure, preventive maintenance programs have been implemented in which vehicles are periodically serviced and routine maintenance is performed, while data regarding the general health and status of the vehicle is collected and recorded. By routinely conducting such preventive maintenance, vehicle subsystems are serviced whether or not such service is needed. This process results in vehicle downtime, significant costs and requires coordination of scheduled maintenance. Again, such problems are exacerbated when a plurality of vehicles, e.g., a fleet of commercial trucks, must be maintained. When maintenance is performed even though the actual health of the vehicle dictates that it is not necessary, overall process or system costs must be absorbed by the owner of the vehicles.
Moreover, often times no diagnostic or prognostic tests are performed as part of these routine service checks, relying instead on the driver or a maintenance specialist to detect fault conditions (e.g., via visual inspection, out-of-the-ordinary noises, etc.). Notwithstanding routine maintenance, often times unanticipated problems go unnoticed and ultimately develop into serious subsystem failures. Clearly, such a system is inefficient and, more importantly, places many of these vehicles at high risk of serious subsystem failure while in operation. This is a hazardous situation that should be avoided if at all way possible. Some on-board vehicle computer systems exist, but these systems are typically limited in scope and do not have the capability to analyze a comprehensive range of specific vehicle components (e.g., steering, brakes, fuel, etc.). In addition, no known system can efficiently detect early subsystem faults and failures or predict the time to failure. For example, some engine crankcase oil monitoring systems integrate time and temperature to determine if the oil needs to be replaced, but do not consider the effect of lubricant contaminants or multiple short cold running trips that never get the engine up to operating temperature.
Therefore, the field of vehicle diagnostics and maintenance systems is in need of an intelligent system that gathers real-time information relating to the health of a plurality specific vehicle subsystems to determine when maintenance is or will be required.
SUMMARY OF THE INVENTION
The present invention overcomes the limitations of prior vehicle diagnostic systems by providing a system of data collection and diagnostic/prognostic modules that collect data pertaining to vehicle subsystems and process the data to facilitate determining a health state of the individual subsystems, as well as a health state of the overall vehicle. The invention also can be utilized to predict trends in performance of the vehicle, thus affording the present system the ability to accurately and reliably predict when the next vehicle component/subsystem will fail. The system preferably includes a master diagnostics unit that is swappable and modular for rapid diagnosis of subsystem problems and efficient storage of vehicle maintenance records. These system features minimize vehicle downtime and maintenance costs without compromising vehicle performance and safety. In addition, by combining the results of subsystem analysis into an overall system (vehicle) model, the accuracy of the health assessment of individual subsystems may be improved. Also, system-level faults such as subsystem interaction problems and specific faults not previously defined may be detected.
More particularly, the subsystem modules are operatively coupled to components within each of the subsystems via an associated set of sensors. The sensors are positioned at suitable points within the subsystems to collect the desired data. The sensors are preferably intelligent sensors that are adaptable to different operating environments, such as those described hereinafter. The subsystem modules are, in turn, electrically coupled to a master diagnostic module that performs overall system health assessment. Because many of the sensors are placed substantially permanently within the components of corresponding subsystems, as are the subsystem modules, the master diagnostic module, in one preferred embodiment, can be quickly removed or inserted into a docking station mounted on the vehicle without having to worry about proper positioning of the sensors or their associated subsystem modules.
In operation, data is preferably collected, processed and stored by the individual subsystem modules, which themselves are capable of processing and diagnosing maintenance concerns in generating subsystem health assessment signals. The master diagnostics module receives the subsystem health assessment signals and, in response, generates and stores in a memory an overall vehicle health assessment. This memory is preferably sufficient to store a substantial amount of raw and/or processed data. As a result, the master diagnostics module may be employed to gather data for extended periods of time, for example, several weeks, several months or several years. Data collection for extended periods of time affords improved accuracy and machine diagnosis as well as substantially facilitating trend analysis of machine performance and failure prediction.
Furthermore, because the sensors are strategically disposed on or within the vehicle subsystems, and more particularly, the subsystem components, as compared to placing the
Amin Himanshu S.
Gerasimow Alexander M.
Hoff Marc S.
Raymond Edward
Reliance Electric Technologies LLC
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