On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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12 results for “PHM”

Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)cs.LG

Paper tackles cyber threats to PHM systems using adversarial examples.

problem Vulnerability of IoT sensors and DL algorithms to cyber attacks.

method Adopted adversarial example crafting techniques from computer vision to PHM domain.

result PHM models are vulnerable to adversarial attacks, leading to inaccurate remaining useful life estimation.

cyber attacks PHM systems adversarial examples DL algorithms IoT sensors

2020-09-21abs ↗pdf ↗

Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognosticsstat.ML

Enhances PHM solutions by augmenting scarce multivariate time series data.

problem Data scarcity in failure prognostics.

method Extends autoregressive models for data augmentation.

result Significantly improves PHM solution performance.

data augmentation autoregressive models failure prognostics PHM time series

2024-10-21abs ↗pdf ↗

Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Managementstat.ML

Paper presents a data preprocessing method for PHM models.

problem Lack of consistent data preprocessing for PHM applications.

method Comprehensive pipeline for sensor data preprocessing.

result Creation of clean data sets for training machinery health state classifiers.

data preprocessing PHM big machinery data-driven models prognostics

2021-10-08abs ↗pdf ↗

Remaining Useful Lifetime Prediction via Deep Domain Adaptationcs.LG

In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to …

2019-07-17abs ↗pdf ↗

On the application of transfer learning in prognostics and health managementcs.LG

Transfer learning improves machine learning models for equipment diagnostics.

problem Limited model performance due to training data mismatch.

method Transfer learning to reuse knowledge from similar tasks.

result Transfer learning enhances model applicability in PHM.

transfer learning prognostics health management machine learning deep learning

2020-07-03abs ↗pdf ↗

Generative Adversarial Networks for Failure Predictioncs.LG

Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evo…

2019-10-04abs ↗pdf ↗

Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimationcs.LG

This paper proposes a multi-head attention model for predicting RUL in IIoT environments.

problem Estimating RUL for complex industrial equipment using IIoT data.

method Multi-Head Attention Mechanism combined with LSTM for multi-dimensional time-series data.

result The proposed model outperforms state-of-the-art models on benchmark datasets.

RUL prediction IIoT Multi-Head Attention LSTM Prognostics

2022-12-12abs ↗pdf ↗

Prognostics Estimations with Dynamic Statescs.LG

The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment. However, they generally suffer from this problem of lacking prior knowledge to …

2018-07-16abs ↗pdf ↗

Deep Detector Health Management under Adversarial Campaignscs.LG

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of…

2019-11-19abs ↗pdf ↗

Remaining Useful Life Estimation Using Functional Data Analysiscs.LG

Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which lever…

2019-04-12abs ↗pdf ↗

Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problemstat.ML

Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.

problem Limited labelled data for RUL prediction in fatigue damage prognosis.

method Pre-training deep learning models on unlabelled sensor data using self-supervised learning.

result Self-supervised pre-trained models significantly outperform non-pre-trained models in RUL prediction with scarce labelled data.

fatigue damage prognostics self-supervised learning data scarcity deep learning

2023-01-20abs ↗pdf ↗

Multi-label Prediction in Time Series Data using Deep Neural Networkscs.LG

Paper tackles fault classification in time series data with deep neural networks.

problem Fault classification over a future horizon in multidimensional time series data with class imbalances.

method Proposes a multi-label recurrent neural network with a new cost function to address class imbalances.

result The proposed algorithm outperforms state-of-the-art techniques in F1-score, precision, and recall.

fault classification time series data deep neural networks class imbalances multi-label prediction

2020-01-27abs ↗pdf ↗