DECK: Experiences on Delta Checkpointing for Industrial Recommendation Systems

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as many models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. In this paper we thus present experiences of developing feature models for two large-scale industrial automation software systems. Specifically, we extended an existing feature modeling tool to support models for different purposes and at multiple levels. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. We further discuss lessons learned during the modeling process.

In this article, a correlation-aided neural network (CANN) framework is proposed for distributed process monitoring of large-scale industrial automation systems. First, to model the nonlinear data relationship of multiple subsystems, a feature space is learned for each subsystem by means of neural network-based nonlinear projection. Second, different from conventional distributed monitoring methods, a distributed learning method and a novel objective function are proposed, where the influence of uncertainties is decreased by considering the correlation of variables among multiple subsystems. Third, a process monitoring method is proposed based on the CANN framework. The impact of the fault is analyzed, and the optimal monitoring performance is achieved by considering the correlation of all subsystems. To further reduce communication overhead among multiple subsystems, a tradeoff between monitoring performance and communication overhead is made based on gradients of the CANN model. Theoretical analysis demonstrates the superiority of the proposed method. The case studies on a multizone heating, ventilation, and air-conditioning system and the Tennessee Eastman benchmark process are given to evaluate and compare the effectiveness of the proposed method.

Architecture-based software reliability analysis methods shall help software architects to identify critical software components and to quantify their influence on the system reliability. Although researchers have proposed more than 20 methods in this area, empirical case studies applying these methods on large-scale industrial systems are rare. The costs and benefits of these methods remain unknown. On this behalf, we have applied the Cheung method on the software architecture of an industrial control system from ABB consisting of more than 100 components organized in nine subsystems with more than three million lines of code. We used the Littlewood/Verrall model to estimate subsystems failure rates and logging data to derive subsystem transition probabilities. We constructed a discrete time Markov chain as an architectural model and conducted a sensitivity analysis. This paper summarizes our experiences and lessons learned. We found that architecture-based software reliability analysis is still difficult to apply and that more effective data collection techniques are required.

After investigating the conception and features of PLC (Programmable Logic Controller) and embedded system, in this paper the conception of a Quasi-PLC is proposed, which combine the advantages of PLC and embedded system. The architecture of Quasi-PLC and a Top-Down design method with collaboration design of software and hardware for Quasi-PLC embedded control system are researched then. Finally, setting the large-scale industrial valve control system as an example, the conception of Quasi-PLC and related technologies are used in industrial valve control system, and a satisfied application effect is obtained, which proves the rationality of the conception of Quasi-PLC and related technologies.

Object-Relational Mapping (ORM) provides developers a conceptual abstraction for mapping the application code to the underlying databases. ORM is widely used in industry due to its convenience; permitting developers to focus on developing the business logic without worrying too much about the database access details. However, developers often write ORM code without considering the impact of such code on database performance, leading to cause transactions with timeouts or hangs in large-scale systems. Unfortunately, there is little support to help developers automatically detect suboptimal database accesses. In this paper, we propose an automated framework to detect ORM performance anti-patterns. Our framework automatically flags performance anti-patterns in the source code. Furthermore, as there could be hundreds or even thousands of instances of anti-patterns, our framework provides sup- port to prioritize performance bug fixes based on a statistically rigorous performance assessment. We have successfully evaluated our framework on two open source and one large-scale industrial systems. Our case studies show that our framework can detect new and known real-world performance bugs and that fixing the detected performance anti- patterns can improve the system response time by up to 98%.

When developing large-scale industrial software systems engineers need to instantiate, configure, and deploy diverse reusable components. The number of component instances required depends on customer requirements only known during configuration and is typically unknown when modeling the systems’ variability. Also, the hierarchy of dynamically created component instances leads to complex dependencies between configuration decisions. Dealing with component multiplicity and hierarchy thus requires an approach capable of expressing the dependencies among dynamically instantiated components and configuration decisions. Furthermore, users need tool support for navigating the complex decision space during configuration. In this experience paper we report on applying a decision-oriented modeling approach for defining component variability, multiplicity, and hierarchy. We further present a configuration tool that guides end users through the complex decision space. We report applications of the approach to industrial software systems and describe patterns and lessons learned.

Adopting an accurate anomaly detection mechanism is crucial for industrial software systems in order to prevent system outages that can deteriorate system availability. However, employing a supervised machine learning technique to detect anomalies in large production scale industrial software systems is highly impractical due to the requirement of annotated data. This raises the need for comprehensive semi-supervised and unsupervised anomaly detection mechanisms. This paper presents the application of Generative Adversarial Network (GAN) based models to detect system anomalies using semi-supervised one-class learning. We show that the use of a variant of GAN known as bidirectional GAN (BiGAN) gives augmented results when compared to the traditional GAN based anomaly detection, for the selected industrial system. Moreover, the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training. The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection, Variational Autoencoders (VAEs) when the size of the feature space increases.

To assure high software quality for large-scale industrial software systems, traditional approaches of software quality assurance, such as software testing and performance engineering, have been widely used within Alibaba, the world's largest retailer, and one of the largest Internet companies in the world. However, there still exists a high demand for software quality assessment to achieve high sustainability of business growth and engineering culture in Alibaba. To address this issue, we develop an industrial solution for software quality assessment by following the GQM paradigm in an industrial setting. Moreover, we integrate multiple assessment methods into our solution, ranging from metric selection to rating aggregation. Our solution has been implemented, deployed, and adopted at Alibaba: (1) used by Alibaba's Business Platform Unit to continually monitor the quality for 60+ core software systems; (2) used by Alibaba's R&D Efficiency Unit to support group-wide quality-aware code search and automatic code inspection. This paper presents our proposed industrial solution, including its techniques and industrial adoption, along with the lessons learned during the development and deployment of our solution.

Critical industrial infrastructures are currently facing increasing cyberspace threats in their underlying information and communication systems. The advanced monitoring, control, and management functionalities of the industrial systems firmly rely on the reliable and secure operations of the industrial control system (ICS) network. This article characterizes the ICS network traffic and presents a scalable and efficient solution for real-time ICS network traffic anomaly detection, considering various forms of ICS anomaly events. The events due to the cyberattacks, malicious operating behaviors, and network anomalies can be effectively detected without sophisticated computational requirements and retrieval of communication protocols. The proposed hybrid statistical-machine learning model integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the abnormal traffic patterns with low false omission rates. The proposed solution is extensively evaluated at a realistic ICS cyber–physical system (CPS) testbed, and the numerical results confirm its high detection accuracy and low computational complexity. Note to Practitioners—This article was motivated by the challenge of real-time anomaly detection in industrial cyber–physical systems (CPSs). The existing industrial control system (ICS) network anomaly detection solutions are generally carried out based on a single model based on the historian database and cannot dynamically classify the abnormal conditions in a real-time fashion. A novel hybrid statistical-machine learning model is developed that integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the anomalous events through traffic pattern analysis. The proposed anomaly detection solution can efficiently provide accurate detection for cyberattacks, malicious operating behaviors, and network anomalies while meeting the real-time requirements of ICS networks. The proposed solution can be deployed in the realistic ICS CPSs, e.g., the power generation system, gas pipeline systems, and urban railway transportation systems. The preliminary numerical results obtained from the ICS-CPS testbed suggested that it can provide high detection accuracy with low computational complexity and, hence, can be adopted with minimal deployment hurdles.

The effect of mesh discretisation on damage and wear predictions using the Discrete Element Method

With the increasing importance of complex software systems in the software industry, the need for using agent technologies to develop large-scale commercial and industrial software systems is growing rapidly. Such systems are complex, and there is a pressing need for system modeling techniques to support reliable, maintainable and extensible design. G-nets are a type of Petri net defined to support the modeling of a system as a set of independent and loosely-coupled modules. In this paper, we first introduce an extension of G-nets - the agent-based G-net - as a generic model for agent design. Then, to progress from an agent-based design model to an agent-oriented model, new mechanisms to support inheritance modeling are introduced. To illustrate our formal modeling technique for multi-agent systems, an example of an agent family in electronic commerce is provided.

Significant equipment like electric arc furnaces (EAFs) and ladle refining furnaces (LRFs) are among the highly dynamic and nonlinear electrical loads found in steel production facilities like Surana Industry. Reactive power imbalance, harmonic distortion, and voltage instability are just a few of the significant power quality issues caused by these complex loads. Because of this, accurate modeling and thorough analysis of these complex power systems are essential to ensuring operational dependability and maintaining power quality. The current literature tends to emphasize mostly steady-state conditions that are frequently not verified empirically with respect to actual operating data, despite the fact that the Electrical Transient Analyzer Program (ETAP) is a common tool used for load flow analysis in industry applications. With an emphasis on accurately simulating the real system behavior under various operating regimes, this paper provides a thorough power flow analysis of the Surana Steel Industry using ETAP. Two 100 MVA, 220/110 kV transformers step down the 110 kV supply that powers the plant, which is drawn from the Chikkasagur substation. The primary loads—a 35-ton rolling mill, a 4 MW EAF, and an LRF are supplemented with a captive power generation system and a harmonic filter to improve power quality and reliability. Under five different operating conditions with different load levels and generator configurations, key performance metrics such as voltage regulation, reactive power flow, harmonic suppression, and generator dynamic response are thoroughly assessed. The power system of the Surana Steel Industry is accurately simulated by the analytical method employed here. Even though a real-time comparison with SCADA data was not attempted, the ETAP model was painstakingly built with detailed equipment specs, typical operating conditions, and performance patterns actually observed at the facility. Accurate simulation of industrial environments, including the complex, unbalanced, and nonlinear loads present in steel mills, was made possible by this all-encompassing approach. Thus, this study shows that when appropriately configured and backed by extensive empirical data, ETAP has a considerable ability to accurately model these difficult environments. These results offer practical information that can be used right away for large-scale industrial power system planning, assessment, and enhancement.

PurposeThe purpose of this paper is to deliberate the system reliability of a system in combination of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance.Design/methodology/approachProbabilistic assessment of complex system consisting three subsystems, multi-failure threats and copula repair approach is used in this study. Abbas Jubrin Bin, V.V. Singh, D.K. Rawal, in this research paper, have analyzed a system consisting of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance.FindingsIn this analysis, four different cases of availability are analysed for Gumbel–Hougaard family copula and also four cases for general repair with similar failure rates are studied. The authors found that when failure rates increase, the system availability decreases, and when the system follows copula repair distribution, the system availability is better than general repair.Research limitations/implicationsThis research may be implemented in various industrial systems where the subsystems are configured under k-out-of-n: G working policy. It is also advisable that copula repair is highly recommended for best performances from the system. On the basis of mean time to system failure (MTSF) computations, the failure rate which affects system failure more needs to be controlled by monitoring, servicing and replacing stratagem.Practical implicationsThis research work has great implications in various industrial systems like power plant systems, nuclear power plant, electricity distributions system, etc. where the k-out-of-n-type of system operation scheme is validated for system operations with the multi-repair.Originality/valueThis work is a new work by authors. In the previously available technical analysis of the system, the researchers have analyzed the repairable system either supplementary variable approach, supplementary variable and system which have two subsystems in a series configuration. This research work analyzed a system with three subsystems with a multi-repair approach and supplementary variables.

Risk management poses a major challenge in the design and operation of large-scale industrial systems. The common strategy for managing risk is the rule-based approach in which automatic safety devices and standard operating procedures are developed as a counter to the potentially catastrophic effects of system malfunctions that can be anticipated. This is, however, a flawed approach. Large-scale industrial systems can be so complex that it is impossible to anticipate all eventualities. In addition, rules are irremedially incomplete as determinants of behavior. They encapsulate explicit knowledge but they cannot be used to completely specify behavior because their use always involves implicit or unarticulated knowledge. A case study of a highly reliable system with catastrophic potential is described. The development of experiential knowledge is seen as a key element in the achievement of safe and reliable procedures. The case study suggests that operators should be given direct access to critical information about the process to be controlled and should be provided with ample opportunity to develop sensitivity to this critical information. >