iTech AI Technology

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.

The technology

How does iTech reason?

Model-based reasoning

Model-based reasoning is used as the basis of all diagnostic applications built in iTech.
It uses the logical connections between components with forward and backward chaining and the optimization techniques described below to dynamically update and optimize its diagnostic strategy or test pattern using its built-in inference engine.

Depending on the configuration and technology in your system (mechanical, electrical, software, BIT) there are optimal methods for building the model. This methodology is well matured and documented thereby enabling you to build effective models quickly and efficiently.

Probabilistic reasoning

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.

Information theory

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.

Inductive reasoning

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.

Laplacian learning

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.

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