Radice: (Italian noun) root Root cause analysis is one of the most crucial operations in software reliability regarding system performance diagnostic. It aims to identify the root causes of system performance anomalies, allowing the resolution or the future prevention of issues that can cause millions of dollars in losses. Common existing approaches relying on data correlation or full domain expert knowledge are inaccurate or infeasible in most industrial cases, since correlation does not imply causation, and domain experts may not have full knowledge of complex and real-time systems. In this work, we define a novel causal domain knowledge model representing causal relations about the underlying system components to allow domain experts to contribute partial domain knowledge for root cause analysis. We then introduce RADICE, an algorithm that through the causal graph discovery, enhancement, refinement, and subtraction processes is able to output a root cause causal sub-graph showing the causal relations between the system components affected by the anomaly. We evaluated RADICE with simulated data and reported a real data use case, sharing the lessons we learned. The experiments show that RADICE provides better results than other baseline methods, including causal discovery algorithms and correlation based approaches for root cause analysis.

Anomaly detection on multivariate time series (MTS) is an important research topic in data mining, which has a wide range of applications in information technology, financial management, manufacturing system, and so on. However, the state-of-the-art unsupervised deep learning models for MTS anomaly detection are vulnerable to noise and have poor performance on the training data containing anomalies. In this article, we propose a novel Self-Training based Anomaly Detection with Generative Adversarial Network (GAN) model called STAD-GAN to address the practical challenge. The STAD-GAN model consists of a generator-discriminator structure for adversarial learning and a neural network classifier for anomaly classification. The generator is learned to capture the normal data distribution, and the discriminator is learned to amplify the reconstruction error of abnormal data for better recognition. The proposed model is optimized with a self-training teacher-student framework, where a teacher model generates reliable high-quality pseudo-labels to train a student model iteratively with a refined dataset so that the performance of the anomaly classifier can be gradually improved. Extensive experiments based on six open MTS datasets show that STAD-GAN is robust to noise and achieves significant performance improvement compared to the state-of-the-art.

In this paper, a ten degrees of freedom (DOF) redundant robot manipulator based on three DOF parallel platforms is introduced. The main contents of the paper include configuration, kinematics, mechanical design and control system. The new robot is a hybrid serial system which is composed of a slide pair and three sections of three DOF parallel platforms. The system is driven by hydraulic cylinders and controlled by electric-hydraulic servo valves and STD Bus. It can sweep large workspace and be used for the operations inside of a large container.