A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes

Qian, Jinchuan; Song, Zhihuan; Yao, Yuan; Zhu, Zheren; Zhang, Xinmin

doi:10.1016/j.chemolab.2022.104711

Cited by 74 publications

(21 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the loss function and optimiser have to be defined. Indeed, no common practice is established (Qian et al, 2022), and it is decided to adopt the MSE (Mean Square Error) as the loss function and the Adam optimiser.…”

Section: Autoencoder Applied To Health State Identificationmentioning

confidence: 99%

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

2023

View full text Add to dashboard Cite

Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor.

show abstract

Section: Autoencoder Applied To Health State Identificationmentioning

confidence: 99%

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

2023

View full text Add to dashboard Cite

show abstract

“…Deep learning, originating from research in artificial neural networks, 18 is capable of modeling intricate data and extracting concealed attributes within data through the construction of more profound architectures, adaptable hidden layers, and non-linear activation functions. 19 Within the domain of industrial process fault diagnosis, deep learning methods based on autoencoders, 20 deep confidence networks, 21 convolutional neural networks 22 and recursive neural networks 23 are widely used. The advantages of deep learning approaches are that feature engineering can be performed automatically without human intervention, 24 which alleviates the reliance on expertise for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Optimized convolutional neural networks for fault diagnosis in wastewater treatment processes

Hu,

Zhang,

Wang

et al. 2024

Environ. Sci.: Water Res. Technol.

View full text Add to dashboard Cite

show abstract

“…By building mathematical models, soft sensors establish the relationship between some easy-to-measure variables X (input) and difficult-to-measure variables Y (output) . Therefore, soft sensors are receiving more and more attention in industrial processes such as fault diagnosis, data monitoring, and quality prediction. − …”

Section: Introductionmentioning

confidence: 99%

Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application

et al. 2023

View full text Add to dashboard Cite

Soft sensors are mathematical methods that describe the dependence of primary variables on secondary variables. A nonlinear characteristic commonly appears in modern industrial process data with increasing complexity and dynamics, which has brought challenges to soft sensor modeling. To solve these issues, a novel supervised attention-based bidirectional long short-term memory (SA-BiLSTM) is first proposed in this paper to handle the nonlinear industrial process modeling with dynamic features. In this SA-BiLSTM model, an attention mechanism is introduced to calculate the correlation between hidden features in each time step, thus avoiding the loss of important information. Furthermore, this approach combines historical quality information and a moving window through a supervised strategy of quality variables. Such manipulation not only extracts and exploits nonlinear dynamic latent information from the process and quality variables but also enhances the model's learning efficiency and overall prediction performance. Finally, two real industrial examples demonstrate the superiority of the proposed method compared to conventional methods.

show abstract

A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes

Cited by 74 publications

References 103 publications

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

Optimized convolutional neural networks for fault diagnosis in wastewater treatment processes

Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application

Contact Info

Product

Resources

About