573 research outputs found
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Condition monitoring plays a significant role in the safety and reliability
of modern industrial systems. Artificial intelligence (AI) approaches are
gaining attention from academia and industry as a growing subject in industrial
applications and as a powerful way of identifying faults. This paper provides
an overview of intelligent condition monitoring and fault detection and
diagnosis methods for industrial plants with a focus on the open-source
benchmark Tennessee Eastman Process (TEP). In this survey, the most popular and
state-of-the-art deep learning (DL) and machine learning (ML) algorithms for
industrial plant condition monitoring, fault detection, and diagnosis are
summarized and the advantages and disadvantages of each algorithm are studied.
Challenges like imbalanced data, unlabelled samples and how deep learning
models can handle them are also covered. Finally, a comparison of the
accuracies and specifications of different algorithms utilizing the Tennessee
Eastman Process (TEP) is conducted. This research will be beneficial for both
researchers who are new to the field and experts, as it covers the literature
on condition monitoring and state-of-the-art methods alongside the challenges
and possible solutions to them
Degradation stage classification via interpretable feature learning
Predictive maintenance (PdM) advocates for the usage of machine learning technologies to monitor asset's health conditions and plan maintenance activities accordingly. However, according to the specific degradation process, some health-related measures (e.g. temperature) may be not informative enough to reliably assess the health stage. Moreover, each measure needs to be properly treated to extract the information linked to the health stage. Those issues are usually addressed by performing a manual feature engineering, which results in high management cost and poor generalization capability of those approaches. In this work, we address this issue by coupling a health stage classifier with a feature learning mechanism. With feature learning, minimally processed data are automatically transformed into informative features. Many effective feature learning approaches are based on deep learning. With those, the features are obtained as a non-linear combination of the inputs, thus it is difficult to understand the input's contribution to the classification outcome and so the reasoning behind the model. Still, these insights are increasingly required to interpret the results and assess the reliability of the model. In this regard, we propose a feature learning approach able to (i) effectively extract high-quality features by processing different input signals, and (ii) provide useful insights about the most informative domain transformations (e.g. Fourier transform or probability density function) of the input signals (e.g. vibration or temperature). The effectiveness of the proposed approach is tested with publicly available real-world datasets about bearings' progressive deterioration and compared with the traditional feature engineering approach
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
Autoencoders have been extensively used in the development of recent anomaly
detection techniques. The premise of their application is based on the notion
that after training the autoencoder on normal training data, anomalous inputs
will exhibit a significant reconstruction error. Consequently, this enables a
clear differentiation between normal and anomalous samples. In practice,
however, it is observed that autoencoders can generalize beyond the normal
class and achieve a small reconstruction error on some of the anomalous
samples. To improve the performance, various techniques propose additional
components and more sophisticated training procedures. In this work, we propose
a remarkably straightforward alternative: instead of adding neural network
components, involved computations, and cumbersome training, we complement the
reconstruction loss with a computationally light term that regulates the norm
of representations in the latent space. The simplicity of our approach
minimizes the requirement for hyperparameter tuning and customization for new
applications which, paired with its permissive data modality constraint,
enhances the potential for successful adoption across a broad range of
applications. We test the method on various visual and tabular benchmarks and
demonstrate that the technique matches and frequently outperforms alternatives.
We also provide a theoretical analysis and numerical simulations that help
demonstrate the underlying process that unfolds during training and how it can
help with anomaly detection. This mitigates the black-box nature of
autoencoder-based anomaly detection algorithms and offers an avenue for further
investigation of advantages, fail cases, and potential new directions.Comment: 16 pages, 4 figures, 4 table
Deep Learning for Decision Making and Autonomous Complex Systems
Deep learning consists of various machine learning algorithms that aim to learn multiple levels of abstraction from data in a hierarchical manner. It is a tool to construct models using the data that mimics a real world process without an exceedingly tedious modelling of the actual process. We show that deep learning is a viable solution to decision making in mechanical engineering problems and complex physical systems.
In this work, we demonstrated the application of this data-driven method in the design of microfluidic devices to serve as a map between the user-defined cross-sectional shape of the flow and the corresponding arrangement of micropillars in the flow channel that contributed to the flow deformation. We also present how deep learning can be used in the early detection of combustion instability for prognostics and health monitoring of a combustion engine, such that appropriate measures can be taken to prevent detrimental effects as a result of unstable combustion.
One of the applications in complex systems concerns robotic path planning via the systematic learning of policies and associated rewards. In this context, a deep architecture is implemented to infer the expected value of information gained by performing an action based on the states of the environment. We also applied deep learning-based methods to enhance natural low-light images in the context of a surveillance framework and autonomous robots. Further, we looked at how machine learning methods can be used to perform root-cause analysis in cyber-physical systems subjected to a wide variety of operation anomalies. In all studies, the proposed frameworks have been shown to demonstrate promising feasibility and provided credible results for large-scale implementation in the industry
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection
Anomaly detection in multivariate time series has emerged as a crucial
challenge in time series research, with significant research implications in
various fields such as fraud detection, fault diagnosis, and system state
estimation. Reconstruction-based models have shown promising potential in
recent years for detecting anomalies in time series data. However, due to the
rapid increase in data scale and dimensionality, the issues of noise and Weak
Identity Mapping (WIM) during time series reconstruction have become
increasingly pronounced. To address this, we introduce a novel Adaptive Dynamic
Neighbor Mask (ADNM) mechanism and integrate it with the Transformer and
Denoising Diffusion Model, creating a new framework for multivariate time
series anomaly detection, named Denoising Diffusion Mask Transformer (DDMT).
The ADNM module is introduced to mitigate information leakage between input and
output features during data reconstruction, thereby alleviating the problem of
WIM during reconstruction. The Denoising Diffusion Transformer (DDT) employs
the Transformer as an internal neural network structure for Denoising Diffusion
Model. It learns the stepwise generation process of time series data to model
the probability distribution of the data, capturing normal data patterns and
progressively restoring time series data by removing noise, resulting in a
clear recovery of anomalies. To the best of our knowledge, this is the first
model that combines Denoising Diffusion Model and the Transformer for
multivariate time series anomaly detection. Experimental evaluations were
conducted on five publicly available multivariate time series anomaly detection
datasets. The results demonstrate that the model effectively identifies
anomalies in time series data, achieving state-of-the-art performance in
anomaly detection.Comment: 16 pages, 9 figure
A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model
Stack autoencoder (SAE), as a representative deep network, has unique and
excellent performance in feature learning, and has received extensive attention
from researchers. However, existing deep SAEs focus on original samples without
considering the hierarchical structural information between samples. To address
this limitation, this paper proposes a new SAE model-neighbouring envelope
embedded stack autoencoder ensemble (NE_ESAE). Firstly, the neighbouring sample
envelope learning mechanism (NSELM) is proposed for preprocessing of input of
SAE. NSELM constructs sample pairs by combining neighbouring samples. Besides,
the NSELM constructs a multilayer sample spaces by multilayer iterative mean
clustering, which considers the similar samples and generates layers of
envelope samples with hierarchical structural information. Second, an embedded
stack autoencoder (ESAE) is proposed and trained in each layer of sample space
to consider the original samples during training and in the network structure,
thereby better finding the relationship between original feature samples and
deep feature samples. Third, feature reduction and base classifiers are
conducted on the layers of envelope samples respectively, and output
classification results of every layer of samples. Finally, the classification
results of the layers of envelope sample space are fused through the ensemble
mechanism. In the experimental section, the proposed algorithm is validated
with over ten representative public datasets. The results show that our method
significantly has better performance than existing traditional feature learning
methods and the representative deep autoencoders.Comment: 17 pages,6 figure
Multimode system condition monitoring using sparsity reconstruction for quality control
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures
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