49 research outputs found
Domain Adaptive Transfer Learning for Fault Diagnosis
Thanks to digitization of industrial assets in fleets, the ambitious goal of
transferring fault diagnosis models fromone machine to the other has raised
great interest. Solving these domain adaptive transfer learning tasks has the
potential to save large efforts on manually labeling data and modifying models
for new machines in the same fleet. Although data-driven methods have shown
great potential in fault diagnosis applications, their ability to generalize on
new machines and new working conditions are limited because of their tendency
to overfit to the training set in reality. One promising solution to this
problem is to use domain adaptation techniques. It aims to improve model
performance on the target new machine. Inspired by its successful
implementation in computer vision, we introduced Domain-Adversarial Neural
Networks (DANN) to our context, along with two other popular methods existing
in previous fault diagnosis research. We then carefully justify the
applicability of these methods in realistic fault diagnosis settings, and offer
a unified experimental protocol for a fair comparison between domain adaptation
methods for fault diagnosis problems.Comment: Presented at 2019 Prognostics and System Health Management Conference
(PHM 2019) in Paris, Franc
Deep feature learning network for fault detection and isolation
Prognostics and Health Management (PHM) approaches typically involve several signal processing and feature engineering steps. The state of the art on feature engineering, comprising feature extraction and feature dimensionality reduction, often only provides specific solutions for specific problems, but rarely supports transferability or generalization: it often requires expert knowledge and extensive intervention. In this paper, we propose a new integrated feature learning approach for jointly achieving fault detection and fault isolation in high-dimensional condition monitoring data. The proposed approach, based on Hierarchical Extreme Learning Machines (HELM) demonstrates a good ability to detect and isolate faults in large datasets comprising signals of different natures, non-informative signals, non-linear relationships and noise. The method includes stacked auto-encoders that are able to learn the underlying high-level features, and a one-class classifier to combine the learned features in an indicator that represents the deviation from the normal system behavior. Once a deviation is identified, features are used to isolate the most deviating signal components. Two case studies highlight the benefits of the approach: First, a synthetic dataset with the typical characteristics of condition monitoring data and different types of faults is applied to evaluate the performance with objective metrics. Second, the approach is tested on data stemming from a power plant generator interturn failure. In both cases, the results are compared to other commonly applied approaches for fault isolation
Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks
Recently, with the development of deep learning, end-to-end neural network
architectures have been increasingly applied to condition monitoring signals.
They have demonstrated superior performance for fault detection and
classification, in particular using convolutional neural networks. Even more
recently, an extension of the concept of convolution to the concept of
kervolution has been proposed with some promising results in image
classification tasks. In this paper, we explore the potential of kervolutional
neural networks applied to time series data. We demonstrate that using a
mixture of convolutional and kervolutional layers improves the model
performance. The mixed model is first applied to a classification task in time
series, as a benchmark dataset. Subsequently, the proposed mixed architecture
is used to detect anomalies in time series data recorded by accelerometers on
helicopters. We propose a residual-based anomaly detection approach using a
temporal auto-encoder. We demonstrate that mixing kervolutional with
convolutional layers in the encoder is more sensitive to variations in the
input data and is able to detect anomalous time series in a better way.Comment: 9 pages, 1 figure, 4 table
Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines
In tunnel construction projects, delays induce high costs. Thus, tunnel
boring machines (TBM) operators aim for fast advance rates, without safety
compromise, a difficult mission in uncertain ground environments. Finding the
optimal control parameters based on the TBM sensors' measurements remains an
open research question with large practical relevance.
In this paper, we propose an intelligent decision support system developed in
three steps. First past projects performances are evaluated with an optimality
score, taking into account the advance rate and the working pressure safety.
Then, a deep learning model learns the mapping between the TBM measurements and
this optimality score. Last, in real application, the model provides
incremental recommendations to improve the optimality, taking into account the
current setting and measurements of the TBM.
The proposed approach is evaluated on real micro-tunnelling project and
demonstrates great promises for future projects.Comment: 17 pages, 5 figures, 3 table
Link dependent origin-destination matrix estimation : nonsmooth convex optimisation with Bluetooth-inferred trajectories
This thesis tackles the traditional transport engineering problem of urban traffic demand estimation by using Bluetooth data and advanced signal processing algorithms. It proposes a method to recover vehicles trajectories from Bluetooth detectors and combining vehicle trajectories with traditional traffic datasets, traffic is estimated at a city level using signal processing algorithms. Involving new technologies in traffic demand estimation gave an opportunity to rethink traditional approaches and to come up with new method to jointly estimate origin-destinations flows and route flows. The whole methodology has been applied and evaluated with real Brisbane traffic data
Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
Partial discharge (PD) is a common indication of faults in power systems,
such as generators, and cables. These PD can eventually result in costly
repairs and substantial power outages. PD detection traditionally relies on
hand-crafted features and domain expertise to identify very specific pulses in
the electrical current, and the performance declines in the presence of noise
or of superposed pulses. In this paper, we propose a novel end-to-end framework
based on convolutional neural networks. The framework has two contributions.
First, it does not require any feature extraction and enables robust PD
detection. Second, we devise the pulse activation map. It provides
interpretability of the results for the domain experts with the identification
of the pulses that led to the detection of the PDs. The performance is
evaluated on a public dataset for the detection of damaged power lines. An
ablation study demonstrates the benefits of each part of the proposed
framework.Comment: 13 pages, 4 figures, 2 table
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation
Origin-Destination Matrix (ODM) estimation is a classical problem in
transport engineering aiming to recover flows from every Origin to every
Destination from measured traffic counts and a priori model information. In
addition to traffic counts, the present contribution takes advantage of probe
trajectories, whose capture is made possible by new measurement technologies.
It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the
information about the flow distribution on links and containing inherently the
ODM assignment. Further, an original formulation of LODM estimation, from
traffic counts and probe trajectories is presented as an optimisation problem,
where the functional to be minimized consists of five convex functions, each
modelling a constraint or property of the transport problem: consistency with
traffic counts, consistency with sampled probe trajectories, consistency with
traffic conservation (Kirchhoff's law), similarity of flows having close
origins and destinations, positivity of traffic flows. A primal-dual algorithm
is devised to minimize the designed functional, as the corresponding objective
functions are not necessarily differentiable. A case study, on a simulated
network and traffic, validates the feasibility of the procedure and details its
benefits for the estimation of an LODM matching real-network constraints and
observations
Learning Informative Health Indicators Through Unsupervised Contrastive Learning
Condition monitoring is essential to operate industrial assets safely and
efficiently. To achieve this goal, the development of robust health indicators
has recently attracted significant attention. These indicators, which provide
quantitative real-time insights into the health status of industrial assets
over time, serve as valuable tools for fault detection and prognostics. In this
study, we propose a novel and universal approach to learn health indicators
based on unsupervised contrastive learning. Operational time acts as a proxy
for the asset's degradation state, enabling the learning of a contrastive
feature space that facilitates the construction of a health indicator by
measuring the distance to the healthy condition. To highlight the universality
of the proposed approach, we assess the proposed contrastive learning framework
in two distinct tasks - wear assessment and fault detection - across two
different case studies: a milling machines case study and a real condition
monitoring case study of railway wheels from operating trains. First, we
evaluate if the health indicator is able to learn the real health condition on
a milling machine case study where the ground truth wear condition is
continuously measured. Second, we apply the proposed method on a real case
study of railway wheels where the ground truth health condition is not known.
Here, we evaluate the suitability of the learned health indicator for fault
detection of railway wheel defects. Our results demonstrate that the proposed
approach is able to learn the ground truth health evolution of milling machines
and the learned health indicator is suited for fault detection of railway
wheels operated under various operating conditions by outperforming
state-of-the-art methods. Further, we demonstrate that our proposed approach is
universally applicable to different systems and different health conditions