On board vehicle diagnostic module using pattern recognition

A component diagnostic system for a motor vehicle having at least one component which emits a signal having a pattern containing information as to whether that component is operating normally or abnormally. The system includes at least one sensor which senses the signal and outputs an electrical signal representative thereof and corresponding to the pattern, a processor coupled to the sensor(s) for processing the electrical signal and determining if the pattern is characteristic of abnormal state of operation of the component, and an output device coupled to the processor for affecting a another system within the vehicle if the component is operating abnormally. The processor preferably is a pattern recognition system.

BACKGROUND OF THE INVENTION 
Every automobile driver fears that his or her vehicle will breakdown at 
some unfortunate time, e.g., when he or she is traveling at night, during 
rush hour, or on a long trip away from home. To help alleviate that fear, 
certain luxury automobile manufacturers provide roadside service in the 
event of a breakdown. Nevertheless, the vehicle driver must still be able 
to get to a telephone to call for service. It is also a fact that many 
people purchase a new automobile out of fear of a breakdown with their 
current vehicle. This invention is primarily concerned with preventing 
breakdowns and with minimizing maintenance costs by predicting component 
failure which would lead to such a breakdown before it occurs. 
When a vehicle component begins to fail, the repair cost is frequently 
minimal if the impending failure of the component is caught early but 
increases as the repair is delayed. Sometimes if a component in need of 
repair is not caught in a timely manner, the component, and particularly 
the impending failure thereof, can cause other components of the vehicle 
to deteriorate. One example is where the water pump fails gradually until 
the vehicle over heats and blows a head gasket. It is desirable, 
therefore, to determine that a vehicle component is about to fail as early 
as possible so as to minimize the probability of a breakdown and the 
resulting repair costs. 
There are various gages on an automobile which alert the driver to various 
vehicle problems. For example, if the oil pressure drops below some 
predetermined level, the driver is warned to stop his vehicle immediately. 
Similarly, if the coolant temperature exceeds some predetermined value, 
the driver is also warned to take immediate corrective action. In these 
cases, the warning often comes too late as most vehicle gages alert the 
driver after he or she can comfortably solve the problem. Thus, what is 
needed is a component failure warning system which alerts the driver to 
the impending failure of a component long before the problem gets to a 
catastrophic point. 
Some astute drivers can sense changes in the performance of their vehicle 
and correctly diagnose that a problem with a component is about to occur. 
Other drivers can sense that their vehicle is performing differently but 
they don't know why or when a component will fail or how serious that 
failure will be. The invention disclosed herein will, in most cases, solve 
this problem by predicting component failures in time to permit 
maintenance and thus prevent vehicle breakdowns. 
Presently, automobile sensors in use are based on specific predetermined 
levels, such as the coolant temperature or oil pressure, whereby an 
increase above the set level or a decrease below the set level will 
activate the sensor, rather than being based on changes in this level over 
time. The rate at which coolant heats up, for example, can be an important 
clue that some component in the cooling system is about to fail. There are 
no systems currently on automobiles to monitor the numerous vehicle 
components over time and to compare component performance with normal 
performance. Nowhere in the vehicle is the vibration signal of a normally 
operating front wheel stored, for example, or for that matter, any normal 
signal from any other vehicle component. Additionally, there is no system 
currently existing on a vehicle to look for erratic behavior of a vehicle 
component and to warn the driver or the dealer that a component is 
misbehaving and is therefore likely to fail in the very near future. 
OBJECTS AND SUMMARY OF THE INVENTION 
Accordingly, it is an object of the present invention to solve the above 
problems by monitoring the patterns of signals emitted from the vehicle 
components and, through the use of pattern recognition technology, 
forecasting component failures before they occur. Vehicle component 
behavior is monitored over time in contrast to currently used systems 
which merely wait until a serious condition occurs. 
It is another object of the present invention to provide a new and improved 
on-board vehicle diagnostic module utilizing pattern recognition 
technologies which are trained to differentiate normal from abnormal 
component behavior. In this manner, the problems discussed above, as well 
as many others, are alleviated by the vehicle diagnostic module described 
in the paragraphs below. 
The diagnostic module in accordance with the invention utilizes information 
which already exists in signals emanating from various vehicle components 
along with sensors which sense these signals and, using pattern 
recognition techniques, compares these signals with patterns 
characteristic of normal and abnormal component performance to predict 
component failure earlier than would otherwise occur if the diagnostic 
module was not utilized. If fully implemented, this invention is a total 
diagnostic system of the vehicle. In most implementations, the module is 
attached to the vehicle and electrically connected to the vehicle data bus 
where it analyzes data appearing on the bus to diagnose components of the 
vehicle. 
Principal objects and advantages of this invention are thus: 
1. To prevent vehicle breakdowns. 
2. To alert the driver of the vehicle that a component of the vehicle is 
functioning differently than normal and might be in danger of failing. 
3. To alert the dealer, or other repair facility, that a component of the 
vehicle is functioning differently than normal and is in danger of 
failing. 
4. To provide an early warning of a potential component failure and to 
thereby minimize the cost of repairing or replacing the component. 
5. To provide a device which will capture available information from 
signals emanating from vehicle components for a variety of uses such as 
current and future vehicle diagnostic purposes. 
6. To provide a device which uses information from existing sensors for new 
purposes thereby increasing the value of existing sensors and, in some 
cases, eliminating the need for sensors which provide redundant 
information. 
7. To provide a device which is trained to recognize deterioration in the 
performance of a vehicle component based on information in signals 
emanating from the component. 
8. To provide a device which analyzes vibrations from various vehicle 
components which are transmitted through the vehicle structure and sensed 
by existing vibration sensors such as vehicular crash sensors used with 
airbag systems. 
9. To provide a device which provides information to the vehicle 
manufacturer of the events leading to a component failure. 
10. To apply pattern recognition techniques based on training to diagnosing 
potential vehicle component failures. 
Other objects and advantages of the present invention will become apparent 
from the following description of the preferred embodiments taken in 
conjunction with the accompanying drawings.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
For the purposes herein the following terms are defined as follows: 
The term "component" refers to any part or assembly of parts which is 
mounted to or a part of a motor vehicle and which is capable of emitting a 
signal representative of its operating state. The following is a partial 
list of general automobile and truck components, the list not being 
exclusive: 
engine; 
transmission; 
brakes and associated brake assembly; 
tires; 
wheel; 
steering wheel; 
water pump; 
alternator; 
shock absorber; 
wheel mounting assembly; 
radiator; 
battery; 
oil pump; 
fuel pump; 
air conditioner compressor; 
differential gear; 
exhaust system; 
fan belts; 
engine valves; 
steering assembly; and 
engine cooling fan assembly. 
The term "sensor" refers to any measuring or sensing device mounted on a 
vehicle or any of its components including new sensors mounted in 
conjunction with the diagnostic module in accordance with the invention. A 
partial, non-exclusive list of common sensors mounted on an automobile or 
truck is: 
airbag crash sensor; 
microphone; 
coolant thermometer; 
oil pressure sensor; 
oil level sensor; 
air flow meter; 
voltmeter; 
ammeter; 
humidity sensor; 
engine knock sensor; 
oil turbidity sensor; 
throttle position sensor; 
wheel speed sensor; 
tachometer; 
speedometer; 
oxygen sensor; 
pitch and roll sensor; 
clock; 
odometer; 
power steering pressure sensor; 
pollution sensor; 
fuel gauge; 
cabin thermometer; 
transmission fluid level sensor; 
yaw sensor; 
coolant level sensor; 
transmission fluid turbidity sensor; 
break pressure sensor; and 
coolant pressure sensor. 
The term "signal" herein refers to any time varying output from a component 
including electrical, acoustic, thermal, or electromagnetic radiation, or 
mechanical vibration. 
Sensors on a vehicle are generally designed to measure particular 
parameters of particular vehicle components. However, frequently these 
sensors also measure outputs from other vehicle components. For example, 
electronic airbag crash sensors currently in use contain an accelerometer 
for determining the accelerations of the vehicle structure so that the 
associated electronic circuitry of the airbag crash sensor can determine 
whether a vehicle is experiencing a crash of sufficient magnitude so as to 
require deployment of the airbag. This accelerometer continuously monitors 
the vibrations in the vehicle structure regardless of the source of these 
vibrations. If a wheel is out of balance, or it there is extensive wear of 
the parts of the front wheel mounting assembly, or wear in the shock 
absorbers, the resulting abnormal vibrations or accelerations can, in many 
cases, be sensed by the crash sensor accelerometer. 
Every component of a vehicle emits various signals during its life. These 
signals can take the form of electromagnetic radiation, acoustic 
radiation, thermal radiation, vibrations transmitted through the vehicle 
structure, and voltage or current fluctuations, depending on the 
particular component. When a component is functioning normally, it may not 
emit a perceptible signal. In that case, the normal signal is no signal, 
i.e., the absence of a signal. In most cases, a component will emit 
signals which change over its life and it is these changes which contain 
information as to the state of the component, e.g., whether failure of the 
component is impending. Usually components do not fail without warning. 
However, most such warnings are either not perceived or if perceived are 
not understood by the vehicle operator until the component actually fails 
and, in some cases, a breakdown of the vehicle occurs. 
In accordance with the invention, each of these signals emitted by the 
vehicle components is converted into electrical signals and then digitized 
(i.e., the analog signal is converted into a digital signal) to create 
numerical time series data which is then entered into a processor. Pattern 
recognition algorithms then are applied in the processor to attempt to 
identify and classify patterns in this time series data. For a particular 
component, such as a tire for example, the algorithm attempts to determine 
from the relevant digital data whether the tire is functioning properly or 
whether it requires balancing, additional air, or perhaps replacement. 
Frequently, the data entered into the computer needs to be preprocessed 
before being analyzed by a pattern recognition algorithm. The data from a 
wheel speed sensor, for example, might be used as is for determining 
whether a particular tire is operating abnormally in the event it is 
unbalanced, whereas the integral of the wheel speed data over a long time 
period (a preprocessing step), when compared to such sensors on different 
wheels, might be more useful in determining whether a particular tire is 
going flat and therefore needs air. In some cases, the frequencies present 
in a set of data is a better predictor of component failures than the data 
itself. For example, when a motor begins to fail due to worn bearings, 
certain characteristic frequencies began to appear. Moreover, the 
identification of which component is causing vibrations present in the 
vehicle structure can frequently be accomplished through a frequency 
analysis of the data. For these cases, a Fourier transformation of the 
data is made prior to entry of the data into a pattern recognition 
algorithm. Other mathematical transformations are also made for particular 
pattern recognition purposes in practicing the teachings of this 
invention. Some of these include shifting and combining data to determine 
phase changes, differentiating the data, filtering the data, and sampling 
the data. Also, there exists certain more sophisticated mathematical 
operations which attempt to extract or highlight specific features of the 
data. This invention contemplates the use of a variety of these 
preprocessing techniques and the choice of which ones is left to the skill 
of the practitioner designing a particular diagnostic module. 
When a vehicle component begins to change its operating behavior, it is not 
always apparent from the particular sensors, if any, which are monitoring 
that component. The output from any one of these sensors can be normal 
even though the component is failing. By analyzing the output of a variety 
of sensors, however, the pending failure can be diagnosed. For example, 
the rate of temperature rise in the vehicle coolant, if it were monitored, 
might appear normal unless it were known that the vehicle was idling and 
not traveling down a highway at a high speed. Even the level of coolant 
temperature which is in the normal range could be in fact abnormal in some 
situations signifying a failing coolant pump, for example, but not 
detectable from the coolant thermometer alone. 
In FIG. 1, a generalized component 100 emitting several signals which are 
transmitted along a variety of paths, sensed by a variety of sensors and 
analyzed by the diagnostic device in accordance with the invention is 
illustrated schematically. Component 100 is mounted to a vehicle 180 and 
during operation it emits a variety of signals such as acoustic 101, 
electromagnetic radiation 102, thermal radiation 103, current and voltage 
fluctuations in conductor 104 and mechanical vibrations 105. Various 
sensors are mounted in the vehicle to detect the signals emitted by the 
component 100. These include a vibration sensor 130 also mounted to the 
vehicle, acoustic sensor 110, electromagnetic radiation sensor 115, heat 
radiation sensor 120, and voltage or current sensor 140. In addition, 
various other sensors 150, 151, 152, 153 measure other parameters of other 
components which in some manner provide information directly or indirectly 
on the operation of component 100. All of the sensors illustrated on FIG. 
1 are connected to a data bus 160. A diagnostic module 170, in accordance 
with the invention, is also attached to the vehicle data bus 160 and 
receives the signals generated by the various sensors. 
As shown in FIG. 1, the diagnostic module 170 has access to the output data 
of each of the sensors which have information relative to the component 
100. This data appears as a series of numerical values each corresponding 
to a measured value at a specific point in time. The cumulative data from 
a particular sensor is called a time series of individual data points. The 
diagnostic module 170 compares the patterns of data received from each 
sensor individually, or in combination with data from other sensors, with 
patterns for which the diagnostic module has been trained to determine 
whether the component is functioning normally or abnormally. 
Central to this invention is the manner in which the diagnostic module 170 
determines a normal pattern from an abnormal pattern and the manner in 
which it decides what data to use from the vast amount of data available. 
This is accomplished using pattern recognition technologies such as 
artificial neural networks and training. The theory of neural networks 
including many examples can be found in several books on the subject 
including: Techniques And Application Of Neural Networks, edited by 
Taylor, M. and Lisboa, P., Ellis Horwood, West Sussex, England, 1993; 
Naturally Intelligent Systems, by Caudill, M. and Butler, C., MIT Press, 
Cambridge Mass. 1990; J. M. Zaruda, Introduction to Artificial Neural 
Systems, West publishing Co., N.Y., 1992 and, Digital Neural Networks, by 
Kung, S. Y., PTR Prentice Hall, Englewood Cliffs, N.J., 1993, all of which 
are included herein by reference. The neural network pattern recognition 
technology is one of the most developed of pattern recognition 
technologies. Newer and more efficient systems are now being developed 
such as the neural network system which is being developed by Motorola and 
is described in U.S. Pat. No. 5,390,136 and patent application Ser. No. 
08/76,602. The neural network will be used here to illustrate one example 
of a pattern recognition technology but it is emphasized that this 
invention is not limited to neural networks. Rather, the invention may 
apply any known pattern recognition technology. A brief description of the 
neural network pattern recognition technology is set forth below. 
Neural networks are constructed of processing elements known as neurons 
that are interconnected using information channels call interconnects. 
Each neuron can have multiple inputs but only one output. Each output 
however is connected to all other neurons in the next layer. The neurons 
in the first layer operate collectively on the input data as described in 
more detail below. Neural networks learn by extracting relational 
information from the data and the desired output. Neural networks have 
been applied to a wide variety of pattern recognition problems including 
speech recognition, optical character recognition, and handwriting 
analysis. 
To train a neural network, data is provided in the form of one or more time 
series which represents the condition to be diagnosed as well as normal 
operation. As an example, the simple case of an out of balance tire will 
be used. Various sensors on the vehicle are used to extract information 
from signals emitted by the tire such as the airbag accelerometer, a 
torque sensor on the steering wheel or the pressure output of the power 
steering system. Other sensors which might not have an obvious 
relationship to tire unbalance are also included such as, for example, the 
vehicle speed or wheel speed. Data is taken from a variety of vehicles 
where the tires were accurately balanced under a variety of operating 
conditions also for cases where varying amounts of unbalance was 
intentionally introduced. Once the data had been collected, some degree of 
preprocessing is usually performed to reduce the total amount of data fed 
to the neural network. In the case of the unbalanced tire, the time period 
between data points might be chosen such that there are at least ten data 
points per revolution of the wheel. For some other application, the time 
period might be one minute or one millisecond. 
Once the data has been collected, it is processed by a neural network 
generating program, for example, if a neural network pattern recognition 
system is to be used. Such programs are available commercially, e.g., from 
NeuralWare of Pittsburgh, Pa. The program proceeds in a trial and error 
manner until it successfully associates the various patterns 
representative of abnormal behavior, an unbalanced tire, with that 
condition. The resulting neural network can be tested to determine if some 
of the input data from some of the sensors, for example, can be 
eliminated. In this way, the engineer can determine what sensor data is 
relevant to a particular diagnostic problem. The program then generates an 
algorithm which is programmed onto a microprocessor. Such a microprocessor 
appears inside the diagnostic module 170 in FIG. 1. Once trained, the 
neural network, as represented by the algorithm, will now recognize an 
unbalanced tire on a vehicle when this event occurs. At that time, when 
the tire is unbalanced, the diagnostic module 170 will output a message to 
the driver indicating that the tire should be now be balanced as described 
in more detail below. The message to the driver is provided by output 
means coupled to or incorporated within the module 170 and may be, e.g., a 
light on the dashboard, a vocal tone or any other recognizable indication 
apparatus. 
Discussions on the operation of a neural network can be found in the above 
references on the subject and are well understood by those skilled in the 
art. Neural networks are the most well known of the pattern recognition 
technologies based on training, although neural networks have only 
recently received widespread attention and have been applied to only very 
limited and specialized problems in motor vehicles. Other non-training 
based pattern recognition technologies exist, such as fuzzy logic. 
However, the programming required to use fuzzy logic, where the patterns 
must be determine by the programmer, render these systems impractical for 
general vehicle diagnostic problems such as described herein. Therefore, 
preferably the pattern recognition systems which learn by training are 
used herein. 
The neural network is the first highly successful of what will be a variety 
of pattern recognition techniques based on training. There is nothing 
which suggests that it is the only or even the best technology. The 
characteristics of all of these technologies which render them applicable 
to this general diagnostic problem include the use of time-based input 
data and that they are trainable. In all cases, the pattern recognition 
technology learns from examples of data characteristic of normal and 
abnormal component operation. 
A diagram of one example of a neural network used for diagnosing an 
unbalanced tire, for example, based on the teachings of this invention is 
shown in FIG. 2. The process can be programmed to periodically test for an 
unbalanced tire. Since this need be done only infrequently, the same 
processor can be used for many such diagnostic problems. When the 
particular diagnostic test is run, data from the previously determined 
relevant sensors is preprocessed and analyzed with the neural network 
algorithm. For the unbalanced tire, using the data from the crash 
accelerometer, the digital acceleration values from the analog to digital 
converter in the crash sensor are entered into nodes 1 through n and the 
neural network algorithm compares the pattern of values on nodes 1 through 
n with patterns for which it has been trained as follows. 
Each of the input nodes is connected to each of the second layer nodes, 
h-1,h-2, . . . , h-n, called the hidden layer, either electrically as in 
the case of a neural computer, or through mathematical functions 
containing multiplying coefficients called weights, in the manner 
described in more detail in the above references. At each hidden layer 
node, a summation occurs of the values from each of the input layer nodes, 
which have been operated on by functions containing the weights, to create 
a node value. Similarly, the hidden layer nodes are in like manner 
connected to the output layer node(s), which in this example is only a 
single node O representing the decision to notify the driver of the 
unbalanced tire. During the training phase, an output node value of 1, for 
example, is assigned to indicate that the driver should be notified and a 
value of 0 is assigned to not doing so. Once again, the details of this 
process are described in above-referenced texts and will not be presented 
in detail here. 
In the example above, twenty input nodes were used, five hidden layer nodes 
and one output layer node. In this example, only one sensor was considered 
and accelerations from only one direction were used. If other data from 
other sensors such as accelerations from the vertical or lateral 
directions were also used, then the number of input layer nodes would 
increase. Again, the theory for determining the complexity of a neural 
network for a particular application has been the subject of many 
technical papers and will not be presented in detail here. Determining the 
requisite complexity for the example presented here can be accomplished by 
those skilled in the art of neural network design. 
Briefly, the neural network described above defines a method, using a 
pattern recognition system, of sensing an unbalanced tire and determining 
whether to notify the driver and comprises the steps of 
(a) obtaining an acceleration signal from an accelerometer mounted on a 
vehicle; 
(b) converting the acceleration signal into a digital time series; 
(c) entering the digital time series data into the input nodes of the 
neural network; 
(d) performing a mathematical operation on the data from each of the input 
nodes and inputting the operated on data into a second series of nodes 
wherein the operation performed on each of the input node data prior to 
inputting the operated on value to a second series node is different from 
that operation performed on some other input node data; 
(e) combining the operated on data from all of the input nodes into each 
second series node to form a value at each second series node; 
(f) performing a mathematical operation on each of the values on the second 
series of nodes and inputting this operated on data into an output series 
of nodes wherein the operation performed on each of the second series node 
data prior to inputting the operated on value to an output series node is 
different from that operation performed on some other second series node 
data; 
(g) combining the operated on data from all of the second series nodes into 
each output series node to form a value at each output series node; and, 
(h) notifying a driver if the value on one output series node is within a 
chosen range signifying that a tire requires balancing. 
This method can be generalized to a method of predicting that a component 
of a vehicle will fail comprising the steps of: 
(a) sensing a signal emitted from the component; 
(b) converting the sensed signal into a digital time series; 
(c) entering the digital time series data into a pattern recognition 
algorithm; 
(d) executing the pattern recognition algorithm to determine if there 
exists within the digital time series data a pattern characteristic of 
abnormal operation of the component; and 
(e) notifying a driver if the abnormal pattern is recognized. 
The particular neural network described and illustrated above contains a 
single series of hidden layer nodes. In some network designs, more than 
one hidden layer is used, although only rarely will more than two such 
layers appear. There are of course many other variations of the neural 
network architecture illustrated above which appear in the referenced 
literature. For the purposes herein, therefore, "neural network" will be 
defined as a system wherein the data to be processed is separated into 
discrete values which are then operated on and combined in at least a two 
stage process and where the operation performed on the data at each stage 
is in general different for each discrete value and where the operation 
performed is at least determined through a training process. 
The implementation of neural networks can take on at least two forms, an 
algorithm programmed on a digital microprocessor or in a neural computer. 
In this regard, it is noted that neural computer chips are now becoming 
available. 
In the example above, only a single component failure was discussed using 
only a single sensor. The diagnostic module 170 contains preprocessing and 
neural network algorithms for a number of component failures. The neural 
network algorithms are generally relatively simple, requiring only a few 
lines of computer code. A single general neural network program can be 
used for multiple pattern recognition cases by specifying different 
coefficients for the various terms, one set for each application. Thus, 
adding different diagnostic checks has only a small affect on the cost of 
the system. Also, the system has available to it all of the information 
available on the data bus. During the training process, the pattern 
recognition program sorts out from the available vehicle data on the data 
bus, those patterns which predict failure of a particular component. 
In FIG. 3, a schematic of a vehicle with several components and several 
sensors is shown in their approximate locations on a vehicle along with a 
total vehicle diagnostic system in accordance with the invention utilizing 
a diagnostic module in accordance with the invention. A flow diagram of 
information passing from the various sensors shown on FIG. 3 onto the 
vehicle data bus and thereby into the diagnostic device in accordance with 
the invention is shown in FIG. 4 along with outputs to a display for 
notifying the driver and to the vehicle cellular phone for notifying the 
dealer, vehicle manufacturer or other entity concerned with the failure of 
a component in the vehicle. FIG. 4 also contains the names of the sensors 
shown numbered on FIG. 3. 
Sensor 1 is a crash sensor having an accelerometer, sensor 2 is a 
microphone, sensor 3 is a coolant thermometer, sensor 4 is an oil pressure 
sensor, sensor 5 is an oil level sensor, sensor 6 is an air flow meter, 
sensor 7 is a voltmeter, sensor 8 is an ammeter, sensor 9 is a humidity 
sensor, sensor 10 is an engine knock sensor, sensor 11 is an oil turbidity 
sensor, sensor 12 is a throttle position sensor, sensor 13 is a steering 
torque sensor, sensor 14 is a wheel speed sensor, sensor 15 is a 
tachometer, sensor 16 is a speedometer, sensor 17 is an oxygen sensor, 
sensor 18 is a pitch/roll sensor, sensor 19 is a clock, sensor 20 is an 
odometer, sensor 21 is a power steering pressure sensor, sensor 22 is a 
pollution sensor, sensor 23 is a fuel gauge, sensor 24 is a cabin 
thermometer, sensor 25 is a transmission fluid level sensor, sensor 26 is 
a yaw sensor, sensor 27 is a coolant level sensor, sensor 28 is a 
transmission fluid turbidity sensor, sensor 29 is brake pressure sensor 
and sensor 30 is a coolant pressure sensor. Other possible sensors include 
a temperature transducer, a pressure transducer, a liquid level sensor, a 
flow meter, a position sensor, a velocity sensor, a RPM sensor, a chemical 
sensor and an angle sensor. 
Consider now some examples. The following is a partial list of potential 
component failures and the sensors from the list on FIG. 4 which might 
provide information to predict the failure of the component: 
Out of balance tires 1,13,14,15,20,21 
Front end out of alignment 1,13,21,26 
Tune up required 1,3,10,12,15,17,20,22 
Oil change needed 3,4,5,11 
Motor failure 1,2,3,4,5,6,10,12,15,17,22 
Low tire pressure 1,13,14,15,20,21 
Front end looseness 1,13,16,21,26 
Cooling system failure 3,15,24,27,30 
Alternator problems 1,2,7,8,15,19,20 
Transmission problems 1,3,12,15,16,20,25,28 
Differential problems 1,12,14 
Brakes 1,2,14,18,20,26,29 
Catalytic converter and muffler 1,2,12,15,22 
Ignition 1,2,7,8,9,10,12,17,23 
Tire wear 1,13,14,15,18,20,21,26 
Fuel leakage 20,23 
Fan belt slippage 1,2,3,7,8,12,15,19,20 
Alternator deterioration 1,2,7,8,15,19 
Coolant pump failure 1,2,3,24,27,30 
Coolant hose failure 1,2,3,27,30 
Starter failure 1,2,7,8,9,12,15 
Dirty air filter 2,3,6,11,12,17,22 
Several interesting facts can be deduced from a review of the above list. 
First, all of the failure modes listed can be at least partially sensed by 
multiple sensors. In many cases, some of the sensors merely add 
information to aid in the interpretation of signals received from other 
sensors. In today's automobile, there are few if any cases where multiple 
sensors are used to diagnose or predict a problem. In fact, there is 
virtually no failure prediction undertaken at all. Second, many of the 
failure modes listed require information from more than one sensor. Third, 
information for many of the failure modes listed can not be obtained by 
observing one data point in time as is now done by most vehicle sensors. 
Usually an analysis of the variation in a parameter as a function of time 
is necessary. In fact, the association of data with time to create a 
temporal pattern for use in diagnosing component failures in automobile is 
unique to this invention. Fourth, the vibration measuring capability of 
the airbag crash sensor is useful for most of the cases discussed above 
yet there is no such current use of this sensor. The airbag crash sensor 
is used only to detect crashes of the vehicle. Fifth, the second most used 
sensor in the above list, a microphone, does not currently appear on any 
automobiles yet sound is the signal most often used by vehicle operators 
and mechanics to diagnose vehicle problems. Another sensor which is listed 
above which also does not currently appear on automobiles is a pollution 
sensor. This is typically a chemical sensor mounted in the exhaust system 
for detecting emissions from the vehicle. It is expected that this and 
other chemical sensors will be used in the future. 
In addition, from the foregoing depiction of different sensors which 
receive signals from a plurality of components, it is possible for a 
single sensor to receive and output signals from a plurality of components 
which are then analyzed by the processor to determine if any one of the 
components for which the received signals were obtained by that sensor is 
operating in an abnormal state. Likewise, it is also possible to provide 
for a multiplicity of sensors each receiving a different signal related to 
a specific component which are then analyzed by the processor to determine 
if that component is operating in an abnormal state. 
The discussion above has centered on notifying the vehicle operator of a 
pending problem with a vehicle component. Today, there is great 
competition in the automobile marketplace and the manufacturers and 
dealers who are most responsive to customers are likely to benefit by 
increased sales both from repeat purchasers and new customers. The 
diagnostic module disclosed herein benefits the dealer by making him 
instantly aware, through the cellular telephone system coupled to the 
diagnostic module or system in accordance with the invention, when a 
component is likely to fail. As envisioned, on some automobiles, when the 
diagnostic module 170 detects a potential failure it not only notifies the 
driver through a display 210, but also automatically notifies the dealer 
through a vehicle cellular phone 220. The dealer can thus phone the 
vehicle owner and schedule an appointment to undertake the necessary 
repair at each parties mutual convenience. The customer is pleased since a 
potential vehicle breakdown has been avoided and the dealer is pleased 
since he is likely to perform the repair work t. The vehicle manufacturer 
also benefits by early and accurate statistics on the failure rate of 
vehicle components. This early warning system can reduce the cost of a 
potential recall for components having design defects. The vehicle 
manufacturer will thus be guided toward producing higher quality vehicles 
thus improving his competitiveness. Finally, experience with this system 
will actually lead to a reduction in the number of sensors on the vehicle 
since only those sensors which are successful in predicting failures will 
be necessary. 
For most cases it is sufficient to notify a driver that a component is 
about to fail through a warning display. In some critical cases, action 
beyond warning the driver may be required. If, for example, the diagnostic 
module detected that the alternator was beginning to fail, in addition to 
warning the driver of this eventuality, the module could send a signal to 
another vehicle system to turn off all non-essential devices which use 
electricity thereby conserving electrical energy and maximizing the time 
and distance that the vehicle can travel before exhausting the energy in 
the battery. 
In the discussion above, the diagnostic module of this invention assumes 
that a vehicle data bus exists which is used by all of the relevant 
sensors on the vehicle. Most vehicles today do not have a data bus 
although it is widely believed that most vehicles will have one in the 
near future. Naturally, the relevant signals can be transmitted to the 
diagnostic module through a variety of coupling means other than through a 
data bus and this invention is not limited to vehicles having a data bus. 
As can be appreciated from the above discussion, the invention described 
herein brings several new improvements to automobiles including, but not 
limited to, the use of pattern recognition technologies to diagnose 
potential vehicle component failures, the use of trainable systems thereby 
eliminating the need of complex and extensive programming, the 
simultaneous use of multiple sensors to monitor a particular component, 
the use of a single sensor to monitor the operation of many vehicle 
components, the monitoring of vehicle components which have no dedicated 
sensors, and the notification of both the driver and possibly an outside 
entity of a potential component failure in time so that the failure can be 
averted and vehicle breakdowns substantially eliminated. 
Although several preferred embodiments are illustrated and described above, 
there are possible combinations using other signals and sensors for the 
components and different forms of the neural network implementation or 
different pattern recognition technologies that perform the same functions 
which can be utilized in accordance with the invention. Also, although the 
neural network has been described as an example of one means of pattern 
recognition, other pattern recognition means exist and still others are 
being developed which can be used to identify potential component failures 
by comparing the operation of a component over time with patterns 
characteristic of normal and abnormal component operation. In addition, 
with the pattern recognition system described above, the input data to the 
system may be data which has been pre-processed rather than the raw signal 
data either through a process called "feature extraction" or by various 
mathematical transformations. This invention is not limited to the above 
embodiments and should be determined by the following claims.