Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift

Yong, Bang Xiang; Brintrup, Alexandra

doi:10.1016/j.asoc.2022.108912

Cited by 5 publications

(1 citation statement)

References 61 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised feature learning auto-encoder systems showed promising results in the recent literature, where Nayeeb et al [19] developed a two-stage auto-encoder-based features enrichment technique to detect COVID-19 from chest X-ray images, and Zhu et al [20] proposed a novel method called Adaptive Aggregation-Distillation AutoEncoder (AADAE) for unsupervised anomaly detection of sensor-based data in order to solve industrial engineering tasks, while also broadening the whole AE applications domain. Other researchers such as Yong et al [21] optimized the adaptability of the AE in sensor-based solutions by proposing novel explanation methods based on the mean and epistemic uncertainty of log-likelihood estimates, and Haosen et al [22] explored a novel data-driven approach for long-term real-time and robust voltage stability assessment based on variational autoencoder (VAE), solving the problem of increased uncertain elements in power systems and the extensive deployment of online monitoring devices. More interestingly, AE-based sensor systems and analytics methods are more frequently being used, even in space exploration agendas.…”

Section: Introductionmentioning

confidence: 99%

Auto-Encoder-Enabled Anomaly Detection in Acceleration Data: Use Case Study in Container Handling Operations

Jakovlev

Vozňák

2022

Machines

View full text Add to dashboard Cite

The sudden increase in containerization volumes around the globe has increased the overall number of cargo losses, infrastructure damage, and human errors. Most critical losses occur during handling procedures performed by port cranes while sliding the containers to the inner bays of the ship along the vertical cell guides, damaging the main metal frames and causing the structure to deform and lose its integrity and stability. Strong physical impacts may occur at any given moment, thus in-time information is critical to ensure the clarity of the processes without halting operations. This problem has not been addressed fully in the recent literature, either by researchers of the engineering community or by the logistics companies’ representatives. In this paper, we have analyzed the conventional means used to detect these critical impacts and found that they are outdated, having no real-time assessment capability, only post-factum visual evaluation results. More reliable and in-time information could benefit many actors in the transportation chain, making transportation processes more efficient, safer, and reliable. The proposed solution incorporates the monitoring hardware unit and the analytics mechanism, namely the auto-encoder technology, that uses the acceleration parameter to identify sensor data anomalies and informs the end-user if these critical impacts occurred during handling procedures. The proposed auto-encoder analytical method is compared with the impacts detection methodology (IDM), and the result indicates that the proposed solution is well capable of detecting critical events by analyzing the curves of reshaped signals, detecting the same impacts as the IDM, while improving the speed of the short-term detection periods. We managed to detect–predict between 9 and 18 impacts, depending on the axis of container sway. An experimental study suggests that if programmed correctly, the auto-encoder (AE) can be used to detect deviations in time-series events in different container handling scenarios.

show abstract