Above: PRO-AID performs real-time monitoring and diagnostics for a Molten Salt Reactor
has a minimum sensor set requirement. To allow the construction of diagnostic models for maximum coverage, the concept of virtual sensors was also introduced to be used in place of missing sensors for model training. In this case a physics-based model was developed and implemented in ANL’s Parameter-Free Reasoning Operator for Automated Identification and Diagnosis (PRO-AID) which performs real-time monitoring and diagnostics for an engineering system. The framework relies on normal (fault-free) models of a system such that any anomalies due to faults can be detected from the inconsistencies between the normal behaviours predicted by the models and observed data. When the system is fault-free, the observed data must satisfy relations imposed by the fault-free system model. Such constraint relations between observations and the expected normal behaviours are formally defined as analytical redundancy relations (ARRs). Each analytical redundancy relation may involve a subset of sensor data and certain parts of the system. In PRO-AID, each ARR is represented by an equation establishing the constraint among the involved sensor readings. The difference between the two sides of the ARR equation is defined as the residual. A non-zero residual would indicate a violation to the ARR, implying at least one of the involved sensors or part of the system is in a fault state.
The set of model residuals forms the basis for fault
diagnostics. Each non-zero residual serves as a fault symptom that implicates certain system faults. That set of relevant faults – the possible causes of the non- zero residuals – can be identified from the underlying ARR. Based on the set of all the residuals generated for a system, various reasoning approaches, including both deterministic and probabilistic reasoning, can be employed to produce fault diagnoses. Faults may be detected and diagnosed at a given time based on observed symptoms. In the presence of measurement and modelling un- certainty, a statistical tool may be needed to determine whether a residual is (statistically) non-zero. To address the objective of integrating LLMs with
PRO- AID, a system was designed consisting of four major components:
● Diagnostics Agent: A large language model that has been engineered to contextualise the plant and data generated by PRO-AID, then query the symbolic engine if additional information is needed by the operator.
● Symbolic Engine: A graph of information that forms the basis of the plant knowledge available to the LLM. Provides various functions to the LLM to query PRO-AID or Plant data.
● PRO-AID: A state-of-the-art monitoring & diagnostics tool. Component and sensor faults are determined by physics-based model that are calibrated with plant data.
● Plant: The physical system that is monitored using sensors. The system is represented digitally via a graph generated from the Piping and Instrumentation Diagram (P&ID).
The explainability of the PRO-AID diagnostic results stemmed from the use of physics-based analytical models. The causal relations between potential faults in the system and possible fault symptoms (non-zero residuals) are derived from the physics-based ARR and stored within PRO-AID. Fault diagnoses are obtained by logical inference based on observed symptoms, knowing the possible causes of each symptom. In the reverse direction, the fault diagnosis can be presented along with the observed symptoms and explained intuitively by causality. Forward chaining can be used by an operator to test that a diagnosis given by the algorithm is logically consistent with the symptoms that led to the diagnosis. The natural tendency of an operator is to do just that and this approach can be facilitated by making available the information an operator will use to check for logical consistency.
Using LLMs to explain results In their research, the authors embed a large language model (LLM) machine learning agent inside a system to explain fault diagnoses to the operator. An LLM can perform various natural language processing tasks and have been embedded in various online portals as chatbots to accommodate arbitrary text-based interactions with humans. While any LLM could be embedded in the
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