Time series anomaly detection is challenging due to the rarity of anomalies and the complexity and diversity of normal patterns. Most existing methods rely on a single hypothesis and learn feature patterns from a limited dataset, which restricts their generalization capabilities. At the same time, time series foundation models have shown promising results across multiple tasks due to their strong generalization capabilities. However, time series foundation models are less likely to achieve better performance in complex anomaly detection tasks. To address this issue, this paper introduces FOCA, a novel foundation-model-based one-class anomaly detection approach. This method preserves the generalization ability of the foundation model to capture normal variation patterns and provides a comprehensive feature space for one-class classification. It constrains normal features within a sufficiently small hypersphere to construct a decision boundary for detecting abnormal data. Furthermore, it is observed in practice that the introduction of fine-tuning techniques can further improve the performance of the method. Extensive experiments on two standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.

Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.

Time-series Anomaly Detection(AD) is widely used in monitoring and security applications in various industries and has become a hot spot in the field of deep learning. Normality-representation-based methods perform well in certain scenarios but may ignore some aspects of the overall normality. Feature-extraction-based methods always take a process of pre-training, whose target differs from AD, leading to a decline in AD performance. In this paper, we propose a new AD method called deep Autoencoding One-Class (AOC), which learns features with AutoEncoder(AE). Meanwhile, the normal context vectors from AE are constrained into a hypersphere small enough, similar to one-class methods. With an objective function that optimizes the two assumptions simultaneously, AOC learns various aspects of normality, which is more effective for AD. Experiments on public datasets show that our method outperforms existing baseline approaches.

Due to the ubiquitous increase of Industrial Internet of Things(IIoT) devices, there is a tendency to move some of the microservices-based applications from Cloud to Edge. However, edge devices are prone to node failures because of weak reliability, resulting in the loss of stateful microservices computing state, which may involve fault tolerance of stateful microservices. Moreover, the method of traditional mechanisms for microservices fault tolerance could not meet the real-time requirement. Within this context, based on stateful microservices characteristics, we propose a novel fault tolerant mechanism for IIoT Edge, which mainly consists of causal logging and distributed checkpoint algorithm. This fault recovery mechanism utilizes causal logging to record the nondeterministic events of microservices, and completes the state recovery of microservices by loading checkpoint and replaying log records, which achieves exactly-once guarantees for distributed microservices. In addition, a set of experiments was performed to evaluate the proposed mechanism by integration with Kubernetes. The results show that the proposed mechanism has less impact on service performance compared with other methods.

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self- supervised deep learning task. Single-normality-assumption- based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. Existing multi-normality-assumption-based methods are usually two-staged, firstly pre-training through certain tasks whose target may differ from AD, limiting their performance. To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification. It treats the original and reconstructed representations as the positive pair of negative-sample-free CL, namely “sequence contrast”. Next, invariance terms and variance terms compose a contrastive one-class loss function in which the loss of the assumptions is optimized by invariance terms simultaneously and the “hypersphere collapse” is prevented by variance terms. In addition, extensive experiments on two real- world time-series datasets show the superior performance of the proposed method achieves state-of-the-art. *The full version of the paper can be accessed at https://arxiv.org/abs/2207.01472

As the Industrial Internet of Things (IIoT) develops, intelligent services applying stream computing, such as industrial robot health management, are requiring higher timeliness of data processing, which may involve scheduling of stream tasks. However, traditional scheduling methods are no longer suitable for the currently widely used cloud-edge collaboration mode, not considering the cloud-edge heterogeneity, and focusing on the scheduling of single tasks instead of the optimization of the total tasks. To improve the performance of the cloud-edge collaboration, this paper establishes a practical model for task scheduling considering respectively cloud-edge environment collaboration models. We propose a novel two-stage scheduling method for IIoT. The algorithm utilizes the idea of maximum flow to divide the task into cloud-edge deployment schemes and find the best partitioning scheme, and then deploy the operator for the edge domain based on the network topology by using dynamic programming. Experimental results show that the proposed method could reduce 7.27% the cloud-edge bandwidth usage compared with the highest greedy algorithm for traffic difference, 24.33% end-to-end latency and 11.18% back-pressure rate compared with SBON.

With the development of the Industrial Internet of Things (IIoT), various industrial intelligent applications are emerging, especially Prognostics and Health Management (PHM). The Health Condition Monitoring(HCM) of machines is an important part of PHM and has an essential purpose to improve the intelligence level of industrial machines. However, traditional monitoring methods are no longer suitable for the current adaptability, latency, bandwidth, and privacy requirements in IIoT. In this paper, we propose a novel data-driven HCM architecture. Firstly, the Knowledge Distillation (KD) and a threshold method are respectively proposed for the cloud-edge collaborative training and inference mechanism. Secondly, a health condition representation method of multisensor signal data is proposed. Finally, experiments on the public rolling element bearing dataset show that our method can significantly improve latency and save bandwidth while ensuring accuracy.

With the development of industrial automation and intelligence deepening, industrial robots' installations increase rapidly. The traditional robot operation and management methods are no longer suitable for current requirements. Cloud computing and big data technology together are deemed to form a promising and possible solution to this challenge. From this point of view, this paper proposes a novel industrial robotics cloud platform architecture, and application orchestration and containerized software appliance technology-enabled cloud-edge collaboration mechanism is designed therein. Two exemplary and real-world case studies, supervisory control and data acquisition, and industrial robot predictive maintenance, are further conducted to demonstrate our architecture's generality and versatility.