CA1231447A - Rule based diagnostic system with dynamic alteration capability - Google Patents

Abstract

ABSTRACT OF THE DISCLOSURE
Sensor based diagnostic apparatus for performing on-line realtime monitoring of an industrial or other operating system. The diagnostic apparatus has a rule network for propagation of belief leading to one or more possible malfunctions of equipment in the operating system.
The propagation of belief is dynamically altered as a function of the operating conditions of the sensors them-selves.

Description

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1 52,016 RULE BASED DIAGNOSTIC SYSTEM WITH
DYNAMIC ALTERATION CAPABILITY
BACKGROUND OF THE INVENTION
Field of the Invention:
The invention in general relates to diagnostic apparatus having on-line sensor inputs, and particularly to apparatus which dynamically takes into account failing sensors.
Description of the Prior Art:
Complex industrial or other operating systems generally have a plurality of sensors for monitoring various parameters during operation, not only for control purposes but for purposes of monitoring the system to detect actual or impending malfunctions.
Some systems may utilize dozens, if not hundreds, of sensors in the diagnostic process and very often the sensors may fail, degrade or provide spurious readings not related to the actual parameter being measured.
Use of erroneous sensor data in the diagnostic process can lead to erroneous conclusions about possible malfunctions. In one respect, a malfunction may be India acted where, in fact, no malfunction exists and conversely a malfunction may be occurring or Jay occur without its detection and without proper notification to the system operator. Such event can represent a tremendous economic loss as well as a potentially dangerous situation.
Diagnostic apparatus has been proposed which utilizes a rule-basec approach for representing diagnostic ~'~

2 52,0~6 rules whereby belief leading to a consequent malfunction is propagated, based upon the sensor readings.
The present invention, inter alias recognizes malfunctioning sensors and functions to dynamically alter the propagation of belief leading to a component malfunc-lion diagnosis by the apparatus thereby reducing the importance of a malfunctioning sensor in the diagnostic process.
SUMMARY OF THE INVENTION
Computer-controlled diagnostic apparatus has stored within the memory of the computer a rule base pertinent to a particular operating system being diagnosed, with the rule base being operable to reach one or more conclusions relative to the condition of the system. The rule base is comprised of a plurality of schema, each being defined by a data structure, having a plurality of slots in which particular attributes of the schema are stored. The rule base includes at least one special rule which is operable to change the contents of any predetermined slot of any predetermined other schema upon the occurrence of a predetermined event and thus dynamically alter the diagnose tic process. In an operating system which has a plurality of sensors for monitoring different parameters of the system, diagnostic apparatus is responsive to the sensor signals to diagnose possible malfunctions of the operating system as well as malfunctions of selected sensors. The special rule will operate in response to possible malfunc-lions of the sensors themselves to reduce or eliminate the malfunctioned sensor's contribution in the diagnostic process.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure l is a block diagram illustrating the monitoring of an operating system;
Figures 2 and 3 illustrate nodal diagrams Llti-lived to explain one type of expert system which may reutilized in the operation of the present invention;

I

3 52,016 Figures PA and 4B illustrate various functions associated with components of Figures 2 and 3;
Figures PA through 5C are representations of data structures utilized in the present invention;
Figure 6 is a simplified diagram of an electrical generator diagram to explain the operation of the present invention with respect to one particular malfunction of the generator;
Figure 7 is a nodal diagram of a portion of an expert system used in the propagation of belief relative to a particular malfunction of the generator illustrated in Figure 6;
Figures PA to YE illustrate certain functions associated with the expert system of Figure 7;
Fig. 9 is a nodal diagram of an expert system fragment illustrating another function of the present invention.
DESCRIPTION OF THE PREFERRED _ BODIMENT
In Figure 1 an industrial or other operating system 10 to be monitored is provided with a plurality of sensors So, So........ Sun each for monitoring a certain operating parameter of the system, with each being operable to provide an output signal indicative of the monitored parameter.
Diagnostic apparatus 12 receives the sensor output signals and in response thereto provides indications of the overall health of the operating system in general, and of its components in particular, and provides such indications to an output device such as a display 15 for presentation to the operator of the system.
The diagnostic apparatus 12 in addition to conventional sensor signal conditioning circuits 13 in-eludes a digital computer lo which in a preferred embody-mint controls the diagnostic process by implementation of an expert system computer program that uses knowledge representations and inference procedures Jo reach conical-sons normally determined by a human expert. A common form ~23~

4 52,016 of knowledge representation is in the form of IF THEN
rules and one such system which may be utilized in the practice of the present invention is PUS (Process Diagnosis System) described in the proceedings of the Eighth Internal tonal Joint Conference on Artificial Intelligence, August, 1983, pages 158-163. Basically, in that system (as well as other expert systems) for each rule there is an antecedent or evidence (the IF portion) as well as a consequent or hypothesis the THEN portion) which can become evidence for other rules. As depicted in Figure 2, evidence 16 is linked to the consequent hypothesis lo by means of rule 20, with the evidence and hypothesis keenest-tuning nodes of the system. Numeral 22 represents a supporting rule of node 16, that is, a rule for which node 16 is a hypothesis. Rule 20 is a supported rule of node 16, that is, a rule for which node 16 is evidence. Like-wise, rule 20 is a supporting rule for node 18. In the system, by way of example, nodes can lake thy form of evidence, hypothesis, malfunctions, sensors and storage-nodes which are nodes capable of storing values input from other nodes and performing some predetermined mathematical operation on the values. Awl of these nodes, along with rules, constitute components or schemata of the system.
Associated with each node is a measure of belief, MY, that the node hypothesis) is true, as jell as a measure of disbelief, MD, which is a measure of belief that the hypothesis is not true. Both factors range on a scale from 0 to 1 and the difference between them, My - MD, yields a certainty or confidence factor OF which ranges from -1 to I where positive numbers represent confidence that the hypothesis is trite and negative numbers represent the belief that the hypothesis is not true; numbers in the vicinity of 0 represent uncertainty.
An e~pPrt (or experts) in the field to which the diagnosis pertains establishes the various rules and relationships, which are stored in the computer's memory I

52,016 and utilized in the diagnostic process. The expert's belief in the sufficiency of the rule is also utilized.
This belief, which represents the experts' opinion as to how the presence of evidence proves the hypothesis, is given a numerical representation designated as a suffix Chinese factor, SF, which ranges from -1 to I where post-live values of SF denote that the presence ox the evidence suggests that the hypothesis is true and negative values denote that the presence of the evidence suggests that the hypothesis is not true.
PUS additionally utilizes the expert's belief in the necessity of the rule, which illustrates to what degree the presence of the evidence is necessary for the hypothe-skis to be true. This necessity belief is given a numeral representation designated as a necessity factor NO which ranges from -1 to I where positive values of NO denote that the absence of evidence suggests that the hypothesis is not true and negative values denote that the absence of the evidence suggests that the hypothesis is true.
Figure 3 illustrates another common arrangement wherein a plurality of rules 24 to 26 connect evidence nodes 28 to 31 to a hypothesis node 32. Element I repro-sets the combining of evidence in a a disjunctive manner, that is, if evidence 30 OR 31 is present, or b) in a conjunct Ye manner, that is, if evidence 30 AND 31 are present. Belief leading to a consequent possible malunc-lion in the system being diagnosed is propagated from evidence to hypothesis in repetitive cycles, at the begin-nine of which the OF, MY and MD values of each node are reset to zero (except for a sensor node where the MY and accordingly the OF is assumed to be I
If the OF of the evidence is positive, then the rule's sufficiency is utilized to propagate belief, whereas if the Of of the evidence is negative, the rule's necessity is utilized; if OF is zero, nothing is done.
Basically, if the evidence OF is positive and the SF is positive, then the MY of the hypothesis is increased;

~33~ to 6 ~2,016 if the SF is negative, then the MD of the hypothesis is increased.
Conversely, if the evidence OF is negative, and the NO positive, then the MD of the hypothesis is in-creased, and if the NO is negative, the MY of the hypothe-skis is increased. By way of example, for the single rule case of Figure 2, if MY and MD are the belief and disbelief in the rule's hypothesis, OF the confidence in the rule's evidence, and SF and NO are the rule's sufficiency and necessity, then:
if OF > 0 and SF > 0:
MY = OF x SF I
if OF > 0 and SF < 0:
MD = OF x (-SF) (2) if OF < 0 and NO > 0:
MD = (-OF) x NO (3) if OF < 0 and NO < 0:
MY = OF x NO I
For the multiple rule case of Figure 3, final values are obtained by examining each rule in sequence end performing the calculations for each rule in accordance with the following where Mold and Mold disbelief in the rules hypothesis before each calculation, OF the confidence in the rule's evidence, SF and NO are the rule's sufficiency and necessity and MBneW and MDneW are the belief and disbelief in the rule's hypothesis after each calculation:
if I > 0 and SF 0:
Brew bold Mold) x OF x SF (5) if OF 0 and SF 0:
new old + (1 - Mold) x OF x (-SF) (6) if OF < 0 and NO > 0:
new Mold + (1 told) x (-OF) x NO (7 if OF 0 and NO 0 new Mold + (1 - Mold x I x NO {8) For a disjunctive logical node (OR function) the highest confidence factor of all of the pieces of evidence ~23~7 7 52,016 may be utilized, whereas if the logical node is conjunctive (AND function) the minimum of all of the confidence factors may be utilized. Alternatively, a weighted average may be utilized.
Thus, by utilizing the appropriate previous equations, a measure of belief and/or disbelief is cowlick-fated for a hypothesis and from these values a confidence factor in the hypothesis is calculated from the relation-ship OF = MY -MD.
A rule's sufficiency (SF) or necessity (NO) may in many instances be expressed as a constant. In other instances, the sufficiency and/or necessity may be ox-pressed as some other function which will generate a sufficiency or necessity factor of a fixed number by evaluating the function for a particular variable. A
common function which may be utilized is a piece-wise linear function, two examples of which are illustrated in Figs. PA and 4B. The Y-axis in these figures represent the SF (or NO) ranging from -1 to +1 on the vertical scale.
The X-axis horizontal scale represents the value of some variable such as a sensor reading or the result of some mathematical operation, by way of example. In Fig. PA, if the variable has a value between 0 and a, or is greater than f, it will generate an SF of -1 whereas if the value is between c and d, it will generate an SF of I Values between a and c or d and f will generate corresponding Ifs between -1 and +1. Fig. 4B represents a piece-wise linear function wherein any variable value greater than b will generate an SF of I any variable value less than -b will generate an SF of -1 and values between b and by will generate a corresponding SF between -1 and I
Another type of useful rule is a reading-transform rule which, when carried out, applies a transform function Jo the value found in the rule's evidence node.
If the evidence node is a sensor, the value is a sensor reading, with appropriate conversion, scaling, etc., performed by the transform if needed.

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8 52,016 The computer 13 of the diagnostic apparatus has in its memory the data structure of all the schemata utilized in the diagnostic process. Figure SPA shows the general arrangement for a schemata data structure as applied to any node in the diagnostic system. A schema has associated with it various properties or attributes known as "slots" and Figure PA illustrates some slots of a generalized node schema structure 40. An explanation of the data stored in the slots is as follows:
NODE
Slot 41 - DESCRIPTION:-A readable description defining the particular nude .
Slot 42 - MY:-Value of measure of belief in the node being true.
Slot 43 - MD:-Value of measure of disbelief in the node being true.
Slot 44 - OF:-Value of the confidence factor calculated from MB-MD.
Slot 45 - SUPPORTING RULES:
List of rules for which this node is an Hope thesis.
Slot 46 - SUPPORTED RULES:-List of rules for which this node is evidence.
Slot 47 - UPDATED:-True if MY and MD have been updated. Slot must be true for node to be used in further belief propagation (i.e. must be true for supported rules to fire).

In a similar fashion, belief-rules also have a particular data structure and a generalized belief-rule schema data structure is illustrated with some of its slots, in Fig. 5B. An explanation of the data stored in the slots is as follows:

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9 52,016 BELIEF RULE
Slot 51 - DESCRIPTION:-A readable description defining the particular rule.
Slot 52 - EVIDENCE:-The name of the rule's evidence (such as a prior hypothesis or sensor node).
Slot 53 - HYPOTHESIS:-The name of the rule's hypothesis which can be either a hypothesis or malfunction).
Slot 54 - SF FUNCTION:-The rule's sufficiency as defined by a particular function such as a piece-wise linear function, or a constant.
Slot 55 - NO FUNCTION:-The rule's necessity as defined by a particular function such as a piece-wise linear function, or a constant.
Slot 56 - SF:-The particular value of sufficiency after evil anion of the SF function.
Slot 57 - NO:-The particular value of necessity after evil lion of the NO function.
Slot 58 - CONTEXT:-Contains the name of a context schema or logical combination of names of contexts. The context schema has a value of 0 phallus or 1 (true) as determined by the conditions in the monitored system. The context (or logical context combing anion) as named in this slot must be true in order for the rule to fire. Otherwise the rule is not used in the diagnostic cycle. Different sets of rules may be utilized during the startup of a piece of machinery than during its normal run. The context may be utilized for applying the proper rules. For example a turbine-generator system may have a context of "on-line"
which would be 1 if the generator's main circuit breakers were closed, and O if not. Other con-texts may be established which are always known to be true and others always known to be false.
Slot 59 - UPDATED:-True if rule has fired.

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lo 52,016 It is seen therefore that the slots contain both mathematical and descriptive information. Descriptive information and known functions or values are entered by the person creating the rule-base into the data structure prior to the diagnostic process while mathematical values such as MY, MD and OF are calculated in accordance with the appropriate ones of the previous equations during the diagnostic process and placed into their appropriate slots.
In the present invention a parametric alteration, or puerility, rule is a special type of rule included in the diagnostic system and operable to modify or change any selected slot in any of the schemata of the diagnostic system depending upon an hypothesis such as the operating conditions of the monitoring sensors to thus allow dynamic modification of the propagation of belief. In general, a plurality of such rules are utilized and a typical puerility rule schema data structure 70 is presented in Figure 5C
illustrating various slots associated with the rule and defined as follows:

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11 52,016 PUERILITY RULE
Slot 71 - DESCRIPTION:-A readable description defining the puerility rule.
Slot 72 - EVIDENCE:-The name of an already defined evidence node which this rule monitors.
Slot 73 - EVIDENCE SLOT:-The name of a particular slot in the evidence node containing a number typically the OF slot.
0 Slot 74/75 - MIN/MAX RANGE:-A real number in the range of -1 to +1 which will cause execution of the puerility rule only if the value in the evidence slot is in the defined range.

5 Slot 76 - TRANSFORM:
A particular function such as a piece-wise linear function which transforms the value of the evidence slot into a desired value for the hypothesis 5 slot.
0 Slot 77 - HYPOTHESIS:-Filled with the name of one or more existing schema which are to be modified by the puerility rule.
Slot 78 - HYPOTHESIS SLOT:-The name of a slot that exists in the designated hypothesis. The value in that slot will be changed from whatever it was before the puerility rule fired to whatever the puerility rule trays-form generates.
Slot 79 - CONTEXT
Contains the name of a context schema or logical combination of names of contexts. The context schema has a value of 0 (false) or 1 (true) as determined by the conditions in the monitored system. The context (or logical context combing anion) as named in this slot must be true in order for the rule to fire. Otherwise the rule is not used in the diagnostic cycle. Different sets of rules may be utilized during the startup of a piece of machinery than during its normal run. The context may be utilized for applying the proper rules. For example a turbine-generator system may have a context of "on-line"
which would be 1 if the generator's main circuit go breakers were closed, and 0 if not. Other con-texts may be established which are always known to be true and others always known TV be false.

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12 52,016 The invention will be described with respect to the diagnosis of a particular malfunction in an electric generator whereby the provision of the puerility rule can dynamically alter the propagation of belief in tune diagnose tic process leading to a consequent possible malfunction.
Figure 6 illustrates a simplified diagram of an electrical generator 82 with a portion of the outer casing broken away to show the rotor 83 and stators core 84. The generator is of the type which is cooled by hydrogen gas flowing through ventilation passages in the rotor and stators and wherein pressure zones 1 to 5 are established, with each zone having a pressure different from its neigh-boring zone.
In order to accomplish this, core seals 86 are utilized to prevent leakage of hydrogen gas between two adjacent pressure zones such that gas flow between the zones is as indicated by the arrows. The clearance between a seal and the adjacent metallic stators core is very small and in the order of thousandths of an inch. During opera-lion, this small clearance may lead to actual intermittent contact between the seal and the core thus creating an arcing condition. This arcing, as well as other types of arcing in the system, results in an RF current in the neutral grounding lead of the generator and can be detected by means of a radio frequency monitor RUM 88 which will provide a corresponding output signal indicative of the arcing condition. A second sensor illustrated in Figure 6 it a generator condition monitor (GYM) 90 through which the hydrogen gas is circulated via conduits 92 and 93 in order to detect the presence of thermally produced particles in the hydrogen atmosphere of the generator. In the event that a seal does contact a metal part resulting in an arcing condition, any oil mist or vapor present in the hydrogen will become ionized to cause articulation which will ye carried by conduit 92 to GYM 90 resulting in an output signal therefrom indicative of the condition. The sensor output signals from RUM 88 and GYM 90 are provided 13 52,016 to the diagnostic computer and the presence of any abnormal signals indicating an arcing condition and a articulating condition will provide some indication relative to the existence of arcing in the core seals.
The RUM sensor as well as the GYM sensor are items well known to those persons swilled in the art and are commercially available in various forms.
Figure 7 illustrates, in the previously described node and rule form, a subsystem established by the diagnose tic process for determining the malfunction of arcing in the core seals of the generator of Figure 6. Sensor nodes 100 and 102 are at the first level of diagnosis and receive respective RUM and GYM readings each of which in the present example normally ranges on a scale from 0 to 100 representing percentage of full scale.
RUM sensor node 100 supports rule 104 which utilizes a piece-wise linear curve to map the RUM reading into a confidence factor that the RUM sensor reading is high, as depicted at node 106. By way of example Figure PA
illustrates a piece wise linear curve which would be stored - in the SF function slot of rule 104. From the example in - figure I wherein RUM reading is plotted on the horizontal scale and SF on the vertical scale, it is seen that if the RUM reading at node 100 has a value greater than 60, then the sufficiency factor will be +1 and, in accordance with one of the previous equations, will result in a confidence factor of I that the reading is in fact high.
Any Feeding below a value of 39 generates a sufficiency factor of -1 resulting in a determination that the RUM reading is definitely not high. Readings between the 39 and 41, and 41 and 60 values will have proportionate SF values in accordance with the curve.
The confidence in a high reading at node 106 is used in further equipment diagnosis only after validation of the reading by consideration of the condition of the RUM
sensor itself.

14 52,016 Thus validation of the high reading is accom-polished by rule 108 having the high sensor reading at node 106 as its evidence and a validated high reading at node 110 as its hypothesis. The SF of rule 108 is initially I
such that in the absence of any sensor malfunction, the confidence in a high sensor reading is passed unaltered from node 106 to node 110.
One way of testing a condition of the sensor is by utilization of rule 112 having a piece-wise linear sufficiency function as illustrated in Figure 8B, wherein sensor reading is plotted on the horizontal axis and SF on the vertical axis. Rule 112 is utilized to determine if the RUM reading of node 100 is within its defined range of 0 to 100. Any readings near or outside of these bounds represents a malfunction of a sensor. Thus in Figure 8B
any reading below 0 or above 100 will generate a suffix Chinese factor of I leading to a 100% confidence in node 114 that the sensor is malfunctioning. Any reading between 1 and 99 generates an SE of -l resulting in a conclusion that the sensor definitely is not malfunctioning.
If there is a malfunction in the sensor the propagation of belief in a high sensor reading must be modified. This modification is accomplished according to the present invention by puerility rule 116 which utilizes the piece-wise linear function of Figure 8C to map the confi-dunce in a malfunctioning sensor into a new constant value for SF function for rule 108. In Figure 8C confidence in a sensor malfunction is plotted on the horizontal axis and new constant value for SF function for rule 108 on the vertical axis. As previously discussed, the SF value is the value of sufficiency after evaluation of the SF lung-lion. Therefore, the puerility rule effectively changes the rule's SE since SF takes on the new constant value of the rule's SF function. With additional reference to a puerility rule structure as in Figure 5C, the curve of Figure 8C
would be stored in the transform slot 76. Evidence slot 72 would contain the name of node 114 while slot 73 would I
52,016 contain the slot corresponding to the evidence's OF (slot 44 of Figure PA). Since, in the example of Figure 7, the paralt-rule is modifying another rule, the hypothesis slot 77 would ye filled in with the name of the rule that is being modified, that is, rule 108, whereas slot 78 would be filled in with the particular slot being modified in the hypothesis (the SF function slot 54 of Figure 5B).
Accordingly, if the confidence in a sensor malfunction is -l, indicating no malfunction, then the sufficiency factor of rule 108 which has a value of +1 remains unchanged. If the confidence factor in the met-function is 0 or greater, indicating a malfunction, then the SF function (and accordingly, the SF) of rule 108 is replaced with a 0 value thereby cutting off propagation of 15 belief in a high sensor reading, from node 106 to node 110.
Confidence factor values between 0 and -l generate proper-tonally different SF functions which do not cut off propagation of belief in a high sensor reading, but reduce this belief.
With respect to the GYM sensor, node 102 supports rule 120 which maps the GYM reading into a confidence factor that the reading is low, as depicted at node 122.
The rule utilizes a piece-wise linear function such as illustrated in Figure ED wherein the GYM reading is plotted on the horizontal axis and SF on the vertical axis. As seen in figure ED any reading below a value of 30 generates a sufficiency factor of Al indicative of an actual low sensor reading, whereas values of 41 or greater generate a sufficiency factor of -l, indicating that the GMC reading 30 is not high. Readings between the 30 and 40 and 40 and 41 values generate proportionally different sufficiency factors.
In the absence of a GYM sensor malfunction the confidence in a low sensor reading is passed unaltered to 35 node 124 via rule 126 which would have an initial suffix Chinese factor of I

I
16 52,016 Propagation of this belief is tempered however in the presence of a malfunctioning GYM sensor. For the example of Figure 7 this determination is made in a manner identical to that with respect to the RUM sensor. That is, rule 128 will cause the generation of a sufficiency factor of +1 if the GYM reading is less than 0 or greater than 100 and a sufficiency factor of -1 if the reading is between 1 and 99 as depicted in the previously described piece-wise linear function of Figure 8B. The generated sufficiency factor is utilized to calculate the belief in a malfunc-toned GYM as depicted at node 130 which supports puerility rule 132 having stored in its transform slot a piece-wise linear function identical to that depicted in Figure 8C.
Rule 126 is the designated hypothesis for puerility rule 132 and the SF function of rule 126 would be the hypothesis slot that is modified in a manner identical to that de-scribed with respect to puerility rule 116 and rule 108.
The validated high RUM sensor reading of node 110 and validated lo GYM sensor reading of node 124 support respective rules 134 and 135 leading to the malfunction node 138 indicative of an arcing in the generator cove seals.
Let it be assumed that both the sufficiency and necessity factors of rules 134 and 135 are equal to 0.5.
Let it further be assumed that both sensor readings are within their allowable ranges and that the RUM reading is below 39 and the GYM reading above 41. Utilizing the piece-wise linear functions and the previously provided equations, the OF for both sensor malfunctions 114 and 130 will be -1 resulting in validated readings at nodes 110 and 124 each of which will have an associated OF of -1 (disbelief in a high RUM reading and disbelief in a low GYM
reading). The confidence factor in malfunction 138 there fore is calculated to be -0.75 very strongly indicating that no malfunction is present since the value is negative.
Suppose that on some subsequent reading the RUM
sensor node 100 receives a value of 62 and the CAM sensor 17 52,016 node 102 receives a value of 10, indicative of an arcing condition. With no sensor malfunction, confidence in the validated high and low sensor reading nodes 110 and 124 are both +1 resulting in a malfunction confidence factor of 5 0.75 at node 138 strongly indicative of an arcing condo-lion.
Let it be assumed that at some subsequent reading the RUM sensor goes off scale to a value of 103. In such instance the sufficiency factor of rule 108 gets set to 0 resulting in a confidence factor of 0 for the validated high RUM sensor reading node 110. Under such conditions, the confidence in the malfunction node 138 is calculated to be 0.5. This confidence as well as all calculated confi-dunces may be converted to a corresponding display signal which is provided to display 15 for presentation to the system operator.
Thus a rule-based diagnostic system has been described wherein belief is propagated leading to a result lent possible malfunction. The propagation ox belief is dynamically altered by taking into account sensor malfunc-lions in the diagnostic process.
In general, the puerility rule is operable to modify any slot or slots in any schema of the overall diagnostic system. The elemental example of Figure 7 serves to demonstrate one aspect of the present invention. Although in Figure 7 the SF function slot of another rule is Moe-fled it is understood that in other applications both SE
and NO functions may be modified to effectively change the rule's SF and NO values. With the puerility rule of the present invention, functions of other schema can be changed as well as descriptive names. Further, propagation of belief may be terminated by disallowing a particular rule to fire. The puerility rule may accomplish this latter function merely by changing the context slot of another rule. For example, the name of the current context may be replaced by a different context who e value is known to be 0 false). Yet another operation of the puerility rule is ~Z3~
18 52,016 illustrated in Figure 9 to which reference is now made.
In many diagnostic processes conclusions are reached, inter alias utilizing the time of occurrence of a certain event. Time, accordingly, is propagated in the subsystem established for the diagnostic-process. Figure 9 illustrates another use of the puerility rule whereby time since the occurrence of a particular event is established and propagated.
Node lS0 represents a sensor node into which time is continuously updated. The time is propagated to node 152 via rule 154. Node 156 represents the occurrence of a predetermined event which is linked to rule 154 by a puerility rule 15~ and linked to node 152 by another puerility rule 159.
In the absence of the event, node 152 is continuously 15 updated with the time propagated by rule 154. Node 162 also receives the time via rule 164 and if the event occurs at time To it is operable to compute the time since the event, that is, time minus To In the absence of the event, current time is propagated to node 152 whereby node 162 subtracts the current time from the current time, yielding a result of 0.
Upon the occurrence of the event, puerility rule 158 fires and is operable, by way of example, to change rule 154's context (slot 58 of Figure 5B) so that it evaluates to zero, thereby cutting off propagation of time, in which case node 152 will store the last received time, core-sponging to the occurrence of the event. This stored time, however, will be propagated to rule 162 only if node 152 is updated. Accordingly, puerility rule 159 is operable to maintain the node's updated slot (slot 47 of Figure AYE in a continuously true state such that the time of occurrence of the event is propagated to node 162 which continuously receives the current time to thereby calculate the time since the event, for further propagation via rule 166 in tune diagnostic process. With respect to a sensor-based diagnostic system, the event may be the occurrence of a drastic change in a particular sensor's reading and wherein I
19 52,016 one piece of evidence leading to a possible sensor malfunc-lion would be the duration of time since the drastic change.
Although Figure 7 illustrates a determination of a sensor malfunction utilizing a single node ~100) and rule (112), it is understood that in complex operating systems being monitored, such determination of sensor malfunctions is reached utilizing many rules and test conditions such as described in cop ending Canadian application Serial No. 484,801, filed June 21, 1985, assigned to the same assignee of the present invention.

Claims (11)

CLAIMS:

1. A method of diagnosing an operating system subject to malfunctions, comprising the steps of:

A; storing a rule base pertinent to the particular operating system being diagnosed and being operable to reach one or more conclusions relative to the condition of said system and being comprised of a plurality of schema, each said schema being defined by a data structure having a plurality of slots in which particular attributes of the schema are stored;
and B) changing the contents of any predetermined slot of any predetermined other schema upon the occurrence of a predetermined event.

2. A method of diagnosing an operating system subject to malfunctions, comprising the steps of:

A) providing a plurality of sensors throughout said operating system to obtain respective signals indicative of predetermined operating parameters of said system;

B) storing a rule base pertinent to the particular operating system being diagnosed;

C) providing said sensor signals as inputs to said rule base to propagate belief leading to at least one particular consequent malfunction of said oper-ating system;

D) diagnosing by said rule base, possible malfunctions of said sensors themselves; and E) modifying said propagation of belief as a function of any sensor malfunction.

3. A method of diagnosing an operating system subject to malfunctions and having a plurality of sensors providing signals related to system parameters, comprising the steps of:

A) storing a rule base pertinent to the particular operating system being diagnosed;

B) providing said sensor signals as inputs to said rule base to diagnose malfunctions of said operat-ing system, based upon said sensor signals said rule base being comprised of a plurality of schema, each said schema being defined by a data structure having a plurality of slots into which particular attributes of the schema are stored; and C) changing the contents of at least one slot in a predetermined other schema upon the occurrence of a predetermined condition of at least one sensor of said plurality.

4. A method according to claim 3 which includes the step of:

A) providing said plurality of schema with a plurality of rules each linking evidence to a consequent hypothesis.

5. A method according to claim 4 which includes the steps or:

A) providing each of said rules with an associated predetermined sufficiency factor numerically defining the degree of belief in the rule's hypoth-esis when the rule's evidence is true;

B) storing said sufficiency factor of the rule's data structure; and wherein C) said step of changing effectively changes the value of another rule's sufficiency factor upon said predetermined malfunction.

6. A method according to claim 4 which includes the steps of:

A) providing each of said rules with an associated predetermined necessity factor numerically defining to what degree the presence of the evidence is necessary for the hypothesis to be true;

B) storing said necessity factor in a slot of the rule's data structure; and wherein C) said step of changing effectively changes the value of another rule's necessity factor upon said predetermined malfunction.

7. A method according to claim 1 which includes the step of:

A) providing said plurality of schema with a plurality of rules each linking evidence to a consequent hypothesis.

8. A method according to claim 7 which includes the step of:

A) providing each said hypothesis with an update slot which must be provided with a true value before said hypothesis will participate in said diagnosis;
and wherein B) said step of changing provides said true value to said update slot of a predetermined hypothesis upon the occurrence of said event.

9. A method according to claim 7 which includes the steps of:

A) providing each of said rules with a context slot, designating a context, where the context may be true or false, and said rules will participate in said diagnosis only if their respective context is true; and B) said step of changing provides at least one sel-ected rule with a context of a known true or false state.

10. A method of on-line diagnosing of an operating system comprising the steps of:

A) connecting a plurality of sensors to said system to obtain sensor signals relating to predetermined parameters of said system, while said system is in operation;

B) providing indications relating to the overall operating condition of said system, based upon said sensor signals;

C) monitoring the operating condition of said sensors themselves;

D) modifying the value of a sensor's input contribu-tion to said diagnosis if the particular sensor itself is in an abnormal operating condition.

11. A method according to claim 10 which includes the step of:

A) utilizing a rule-based expert system to provide said indications relating to the overall operating condition of said system.