Related papers: Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes

Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes

URL: http://arxiv.org/abs/2403.16377v1
Date: Mon, 25 Mar 2024 02:47:29 GMT
Title: Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes
Authors: Seokhyun Chung, Raed Al Kontar,
Abstract summary: Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Current methods suffer from a trade-off between representation power and agility in online settings. We propose a neural process-based approach that addresses this trade-off.
Score: 3.10770247120758
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However, this simplification leads to vulnerability to model specifications and an inability to capture complex signals. On the other hand, approaches based on over-parameterized or non-parametric models can excel at explaining complex nonlinear signals, but real-time updates for such models pose a challenging task. In this paper, we propose a neural process-based approach that addresses this trade-off. It encodes available observations within a CM signal into a representation space and then reconstructs the signal's history and evolution for prediction. Once trained, the model can encode an arbitrary number of observations without requiring retraining, enabling on-the-spot real-time predictions along with quantified uncertainty and can be readily updated as more online data is gathered. Furthermore, our model is designed to incorporate qualitative information (i.e., labels) from individual units. This integration not only enhances individualized predictions for each unit but also enables joint inference for both signals and their associated labels. Numerical studies on both synthetic and real-world data in reliability engineering highlight the advantageous features of our model in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.

Related papers

AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency [6.7236795813629]
In streaming setups data flows are affected by factors that yield non-stationarities in the patterns (concept drift) We propose a novel approach, AiGAS-dEVL, which relies on growing neural gas to characterize the distributions of all concepts detected within the stream over time. Our approach exposes that the online analysis of the behavior of these points over time facilitates the definition of the evolution of concepts in the feature space.
arXiv Detail & Related papers (2024-07-07T14:04:57Z)
Root Causing Prediction Anomalies Using Explainable AI [3.970146574042422]
We present a novel application of explainable AI (XAI) for root-causing performance degradation in machine learning models. A single feature corruption can cause cascading feature, label and concept drifts. We have successfully applied this technique to improve the reliability of models used in personalized advertising.
arXiv Detail & Related papers (2024-03-04T19:38:50Z)
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
arXiv Detail & Related papers (2023-03-24T16:03:21Z)
Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
arXiv Detail & Related papers (2022-08-24T07:50:43Z)
Automated Learning of Interpretable Models with Quantified Uncertainty [0.0]
We introduce a new framework for genetic-programming-based symbolic regression (GPSR) GPSR uses model evidence to formulate replacement probability during the selection phase of evolution. It is shown to increase interpretability, improve robustness to noise, and reduce overfitting when compared to a conventional GPSR implementation.
arXiv Detail & Related papers (2022-04-12T19:56:42Z)
Data-driven detector signal characterization with constrained bottleneck autoencoders [0.0]
deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.
arXiv Detail & Related papers (2022-03-09T09:46:10Z)
TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
arXiv Detail & Related papers (2022-02-07T21:37:29Z)
Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs [56.48498624951417]
This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
arXiv Detail & Related papers (2021-04-27T18:17:42Z)
On the benefits of robust models in modulation recognition [53.391095789289736]
Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. In other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations. We propose a novel framework to test the robustness of current state-of-the-art models.
arXiv Detail & Related papers (2021-03-27T19:58:06Z)
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization [67.62847721118142]
We evaluate models' ability to capture the correct vulnerability signals to produce their predictions. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR) The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric.
arXiv Detail & Related papers (2020-11-25T20:05:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.