Unsupervised deep learning-based process monitoring methods

Fouzi Harrou, Ying Sun, Amanda S. Hering, Muddu Madakyaru, Abdelkader Dairi

Research output: Chapter in Book/Report/Conference proceedingChapter


In this chapter, first we provide an overview of some of the shallow-machine learning approaches used in anomaly detection and outlier detection in data mining, namely data clustering techniques. Then, we give a brief description of two frequently used unsupervised machine learning algorithms for one-class classification or detection, namely one-class SVM and support vector data description (SVDD). Particular attention is paid to deep learning models. We present the commonly used deep learning models based on autoencoders (Variational Autoencoder, Denoising Autoencoder, and Contrastive Autoencoder), probabilistic models (Boltzmann Machine and Restricted Boltzmann Machine) and deep neural models (Deep Belief Network and Deep Boltzmann Machine), and we show their capacity and limitations. Finally, we merge the desirable properties of shallow learning approaches, such as one-class support vector machine and k-nearest neighbors and unsupervised deep-learning approaches to develop more sophisticated and efficient monitoring techniques.
Original languageEnglish (US)
Title of host publicationStatistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Number of pages31
ISBN (Print)9780128193655
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-03-02


Dive into the research topics of 'Unsupervised deep learning-based process monitoring methods'. Together they form a unique fingerprint.

Cite this