aiqbench analysis
This paper proposes a data-driven fault diagnosis method that trains a neural network on healthy plant behavior, then uses linear regression to estimate how actuators currently affect sensors while the network stays fixed. It evaluates the approach on two datasets but gives no details here on accuracy, baselines, or scale, so its practical value for industrial settings is still unproven. For ML readers, the main takeaway is the design choice of separating a frozen nonlinear predictor from a simple linear actuator model, which may be useful for diagnosis where labeled faults are scarce.
LaplaceDiag: Fault Diagnosis through Changes in Learned Actuator Effects
arXiv:2611.03849v1 Announce Type: cross Abstract: Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a plant and different faults can produce similar observations. Detailed component models and labeled fault examples may also be unavailable. We introduce LaplaceDiag, a data-driven method that uses changes in actuator effects to narrow the search for faulty equipment. Actuators act on specific devices and mechanisms, so their changed effects can provide clues to root causes. LaplaceDiag learns a healthy plant model with LaplaceNet, a neural network that operates in Laplace space. It separates nonlinear prediction based on measured history from an explicit matrix that describes how actuator values affect sensor values. Keeping the history predictor fixed allows current actuator effects to be estimated by linear regression without retraining the neural network. Comparison with a healthy reference accounts for estimation uncertainty and normal variation to detect changes and generate actuator-related diagnostic hypotheses. The method supports online and offline diagnosis, providing an intermediate step between anomaly detection and identifying faulty components. We successfully evaluate LaplaceDiag on two datasets.
Abstract:Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a plant and different faults can produce similar observations. Detailed component models and labeled fault examples may also be unavailable. We introduce LaplaceDiag, a data-driven method that uses changes in actuator effects to narrow the search for faulty equipment. Actuators act on specific devices and mechanisms, so their changed effects can provide clues to root causes. LaplaceDiag learns a healthy plant model with LaplaceNet, a neural network that operates in Laplace space. It separates nonlinear prediction based on measured history from an explicit matrix that describes how actuator values affect sensor values. Keeping the history predictor fixed allows current actuator effects to be estimated by linear regression without retraining the neural network. Comparison with a healthy reference accounts for estimation uncertainty and normal variation to detect changes and generate actuator-related diagnostic hypotheses. The method supports online and offline diagnosis, providing an intermediate step between anomaly detection and identifying faulty components. We successfully evaluate LaplaceDiag on two datasets.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2611.03849 [cs.AI]
(or arXiv:2611.03849v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.03849 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 268 words