iTech AI for Equipment Troubleshooting admin
iTech – when you can’t tolerate downtime or delays
Troubleshooting complex systems
In most cases when a fault develops on a complex system, current monitoring systems will generate a string of error codes. It is then up to the engineer to interpret these alarms, error codes and messages and troubleshoot the problem. This can still take hours, as he has to access the manual, read the circuit diagrams, check control signals, perform electrical, hydraulic and mechanical tests all the while keeping track of what he is doing and slowly narrowing down the problem to the root cause.
Normal monitoring systems just monitor the status of on-board components, but don’t do any active diagnosis.
iTech reduces downtime
With iTech, you get a built-in expert. The entire logic and all the device information and configurations are stored in the diagnostic database. Therefore when a fault occurs the system goes into diagnostic mode.
If there are any alarms present the system loads them into its logic and immediately knows what they mean. From this it can identify the root alarm.
Once the root alarm is known, the system can identify a list of suspects devices, all those devices that could be causing that alarm or symptom.
iTech can now query the control program for any additional information on inputs and outputs (automated testing). This information is used to further eliminate certain suspect devices and reduce the list of remaining items to test.
Finally it can guide the engineer through a list of manual tests with detailed test instructions of the remaining suspect devices until the root cause of the failure is identified, isolated and rectified.
Some of the theory behind – iTech
iTech can interactively guide a novice technician through each step of an actual troubleshooting session. iTech’s interactive advice is based upon Claude Shannon’s Information Theory; and combines: (a) the Cost(or time) of tests and setup procedures; and (b) the Probability of isolating the fault.
After deducing possible faults for test results and symptoms, iTech concludes automatic Expert Rule generation by inducing probabilities for these possible faults using a statistical Reliability Database (or, Failure Rate Database). iTech automatically builds and maintains this internal Reliability Database.
Each time that iTech is used, it remembers what has failed. It uses this to automatically re-adjust both its internal Reliability Database and its generated Expert Rules. This unique iTech learning algorithm is based upon the work by the famed mathematician Pierre-Simon Laplace.
In troubleshooting, iTech methodically rules out possible faults for test results and symptoms, until the actual faults are found. Each time a test result or symptom is input, iTech uses Non-Monotonic Logic (a form of Meta-Reasoning) to re-focus and reorganise its internal beliefs about where the fault can be.
For example, one Non-Monotonic Meta-Rule, which iTech uses, is:
“If it’s not A, then it must be either B, C, D, ….., or Z”
iTech provides the Knowledge Engineer with Artificial Intelligence “Frames” for defining stereotypical UUT modules, connections and test points. Frames are much like macros in standard programming languages.
Frames are particularly useful for defining standard default values for these stereotypical UUT modules, connections and test points.
iTech is unique in several ways. One way is: iTech does not require any Expert Rules at all. iTech can automatically generate missing Expert Rules. iTech generates expert rules in two steps. In the first step, iTech deduces possible faults for symptoms and test results from the hierarchical structure of the Unit Under Test (UUT). This type of reasoning from structure is known as logic modelling, and lies at the heart of the iTech system. iTech obtains this structural information from a “net-list” of connections produced by the model developer interface
The key to any Expert System is the correct calculation of probabilities (or, certainty factors). In iTech, probabilities are crucial since they are used to guide both the troubleshooter (using Information Theory) and ATE (using Heuristic Search).
In iTech, the probabilities are based upon a generalisation of classical Bayesian Probability Theory. This generalisation – called Dempster-Schafer Probability Theory – models the uncertainty, which is inescapable when modelling unknown problems (i.e. faults) in very complex systems (e.g. military electronics or automobiles).
iTech, for example, uses Dempster’s Rule (for combining multiple symptoms and test results) to model the possibility of multiple failures.