Scalable anomaly detection in manufacturing systems using an interpretable deep learning approach

Anomaly detection in manufacturing systems has great potential for the prevention of critical quality faults. In recent years, unsupervised deep learning has shown to frequently outperform conventional methods for anomaly detection. However, tuning, deploying and debugging deep learning models is a time-consuming task, limiting their practical applicability in manufacturing systems. We approach this problem by developing a deep learning model that learns interpretable shapes that can be used for anomaly detection in temporal process data. Application of the model to assembly tightening processes in the automotive industry shows a significant improvement in model interpretability and scalability

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Anomaly Detection for Vision-based Railway Inspection

none7nomixedRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita CucchiaraRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiar

Microstructural and electrochemical properties of impregnated La0.4Sr0.6Ti0.8Mn0.2O3±d into a partially removed Ni SOFC anode substrate

The microstructural and electrochemical properties of anodes obtained by impregnation of the La0.4Sr0.6Ti0.8Mn0.2O3±d (LSTM) oxide system into two types of anode substrates such as Ni/ 8YSZ substrate (Ni (E)/ 8YSZ) and partially Ni removed Ni/ 8YSZ substrate (Ni(R)/8YSZ) were investigated in order to apply them as anode material for solid oxide fuel cells. All of the samples with LSTM impregnated on Ni (R)/ 8YSZ show higher electrical conductivity values than those of unimpregnated Ni (E)/ 8YSZ under dry H2 condition. The highest electrical conductivity values of 2041.2, 1877.4, and 1764.3 S/cm at 700, 800 and 900 °C can be achieved by samples with 3 wt% impregnated LSTM on Ni (R)/ 8YSZ. From the XPS analysis, the existence of a Ti metal peak on the surface of LSTM was only measured for the LSTM (3 wt%)-Ni (R)/ 8YSZ sample, metallic titanium on the surface can improve the electrical catalytic reaction. LSTM (3 wt%)-Ni (R)/ 8YSZ showed higher electrical conductivity values then those of LSTM (3 wt%)-Ni (E)/ 8YSZ in all the temperature ranges measured in the case of dry CH4 supply. Finally, the electrical conductivity of LSTM (3 wt%)-Ni (R)/ 8YSZ was stably maintained even when exposed to dry CH4 condition at 900 °C for a long time (100 h). © 2020 Elsevier B.V

GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1 loss---trained on 3 healthy slices to reconstruct the next 3 ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late stage much more accurately with AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics (LNBI) as a volume in the Springer serie

X-ray photoelectron spectroscopic study of impregnated La0.4Sr0.6Ti0.8Mn0.2O3±d anode material for high temperature-operating solid oxide fuel cell

In this study, the chemical states of a powder type and an impregnated type of the La0.4Sr0.6Ti0.8Mn0.2O3±d (LSTM) oxide system were investigated along with its electrical conductivities in order to apply these materials as alternative anode materials for high temperature-operating Solid Oxide Fuel Cells (HT-SOFCs). The Ni/8YSZ samples with LSTM impregnated into the pores created by partially removing nickel, Ni/8YSZ (Ni (R)/8YSZ), showed much higher electrical conductivity values than those of unimpregnated Ni/8YSZ (Ni (E)/8YSZ) samples under dry H2 fuel condition. Reduction of Mn4+ to Mn3+ was observed when LSTM was reduced. Additional reduction properties of Mn2+ from Mn3+ and satellite peaks were found when impregnated LSTM was coated onto a Ni/8YSZ substrate. The reduction of the charge state of Ti contained in LSTM showed the same behavior as the reduction property of Mn. However, a satellite peak identified as metal Ti was only observed when impregnated LSTM was coated on a selectively Ni-removed Ni/8YSZ (Ni (R)/8YSZ) substrate

Building ProteomeTools based on a complete synthetic human proteome.

We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of \u3e330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to \u3e1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange

Specialized inpatient treatment of adult anorexia nervosa: effectiveness and clinical significance of changes

Background: Previous studies have predominantly evaluated the effectiveness of inpatient treatment for anorexia nervosa at the group level. The aim of this study was to evaluate treatment outcomes at an individual level based on the clinical significance of improvement. Patients' treatment outcomes were classified into four groups: deteriorated, unchanged, reliably improved and clinically significantly improved. Furthermore, the study set out to explore predictors of clinically significant changes in eating disorder psychopathology. Methods: A total of 435 inpatients were assessed at admission and at discharge on the following measures: body-mass-index, eating disorder symptoms, general psychopathology, depression and motivation for change. Results: 20.0-32.0% of patients showed reliable changes and 34.1-55.3% showed clinically significant changes in the various outcome measures. Between 23.0% and 34.5% remained unchanged and between 1.7% and 3.0% deteriorated. Motivation for change and depressive symptoms were identified as positive predictors of clinically significant changes in eating disorder psychopathology, whereas body dissatisfaction, impulse regulation, social insecurity and education were negative predictors. Conclusions: Despite high rates of reliable and clinically significant changes following intensive inpatient treatment, about one third of anorexia nervosa patients showed no significant response to treatment. Future studies should focus on the identification of non-responders as well as on the development of treatment strategies for these patients