Industrial Data Acquisition Research Articles

Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT

Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.

Data losses and synchronization according to delay in PLC-based industrial automation systems

PLC (Programmable Logic Controllers) based data collection is integral to industrial automation and data acquisition processes. The pipeline – between the device and the database – is a complex system with many components and often causes some time delay, due to the synchronization and the applied hardware and software components. This delay can also lead to data loss on the database site. In this study, we aimed to examine this problem connected to the synchronized behavior of four counter networks within the TIA software for PLCs, particularly focusing on the implications of a 1 Hz (Hz) clock frequency on counter synchronization. Meticulous experiments were conducted and the integration of Node-RED, as an instrumental tool in industrial automation, facilitated counter-behavior monitoring. Recorded values of counter and timestamps were meticulously stored in four separate databases (MSSQL, MySQL, MongoDB, and Apache Cassandra) for comprehensive data analysis. Throughout the experiments, inconsistencies in the counter values were encountered, leading to the discovery of missing values that varied across the ten tests. To detect the reason, a unique delay calculation method was developed. According to our results, we were able to do post-synchronization with millisecond-level accuracy. It can help reveal the missing values that depend on the Node-RED and the PLC cycle time differences.

Design of industrial equipment data acquisition system based on ZYNQ

Abstract In this paper, an industrial equipment data acquisition system based on a ZYNQ chip is constructed using an AD7607 data acquisition module containing a symmetric layout. Next, four modules of signal output, data writing, caching and communication are designed. Focusing on the design of the signal processing system after analog-to-digital conversion, the D-S evidence processing algorithm is utilized to judge the obtained data independently and fuse the data based on its D-S combination rule. Finally, the industrial equipment acquisition system design is tested, and the test includes system performance, accuracy, and stability. The frequency peaks obtained by the system in this paper are all 1000.000Hz, as shown by the results. The signal peak value of 95.18mV, the NI acquisition card signal peak value of 98.33mV, and the error is only 0.89%. The signal-to-noise ratio of the sampled signal is 50.12dB, and the effective number of bits reaches 10.40 bits. The performance of the system and the accuracy and stability of data acquisition on industrial equipment are verified.

Outage Analysis of Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer System for Industrial Emergency Applications.

In the scenario of a natural or human-induced disaster, traditional communication infrastructure is often disrupted or even completely unavailable, making the employment of emergency wireless networks highly important. In this paper, we consider an industrial Supervisory Control and Data Acquisition (SCADA) system assisted by an unmanned aerial vehicle (UAV) that restores connectivity from the master terminal unit (MTU) to the remote terminal unit (RTU). The UAV also provides power supply to the ground RTU, which transmits the signal to the end-user terminal (UT) using the harvested RF energy. The MTU-UAV and UAV-RTU channels are modeled through Nakagami-m fading, while the channel between the RTU and the UT is subject to Fisher-Snedecor composite fading. According to the channels' characterization, the expression for evaluating the overall probability of outage events is derived. The impact of the UAV's relative position to other terminals and the amount of harvested energy on the outage performance is investigated. In addition, the results obtained based on an independent simulation method are also provided to confirm the validity of the derived analytical results. The provided analysis shows that the position of the UAV that leads to the optimal outage system performance is highly dependent on the MTU's output power.

Industrial Internet Data Collection and Processing Technology Research

With the rapid development of the Industrial Internet of Things, data acquisition and processing technology has become a research hotspot. This paper focuses on the industrial Internet data acquisition and processing technology. In view of the current situation and existing problems of industrial Internet data collection technology, such as slow transmission speed, low data quality, poor security, etc., from the three aspects of data transmission, data processing and data security. Specifically, the transmission mode based on the Internet of Things, distributed data processing technology and encryption algorithm and other technical means are adopted to improve the speed and quality of data transmission and ensure the data security. The research results of this paper provide reference and reference for the further development of industrial Internet data acquisition and processing technology, which has certain theoretical and practical significance.

Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure.

Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.

Low-Latency Anomaly Detection on the Edge-Cloud Continuum for Industry 4.0 Applications: the SEAWALL Case Study

Several emerging Industry 4.0 applications related to the monitoring and fault diagnostic of critical equipment introduce strict bounds on the latency of the data processing. Edge computing has emerged as a viable approach to mitigate the latency by offloading tasks to nodes nearby the data sources; at the same time, few industrial case studies have been reported so far. In this paper, we describe the design, implementation and evaluation of the SEAWALL platform for the heterogeneous data acquisition and low-latency processing in Industry 4.0 scenarios. The framework has been developed within the homonymous project founded by the Italian BIREX industrial consortium and involving both academic and industrial partners. The proposed framework supports data collection from heterogeneous production line machines mapped to different IoT protocols. In addition, it enables the seamless orchestration of workloads in the edge-cloud continuum so that the latency of the alerting service is minimized requirement of the processing task is continuously met, while taking into account the constrained resources of the edge servers. We evaluate the SEAWALL framework in a small-case industrial testbed and quantify the performance gain provided by the dynamic workload allocation on the continuum.

SIRDAM4.0: A Support Infrastructure for Reliable Data Acquisition and Management in Industry 4.0

The main claim of the Industry 4.0 manifesto (I4.0) is the promise of a significant transformation of production and delivery processes due to the digitization of manufacturing. A mandatory requirement of the I4.0 transition is the pervasive adoption of Information Technologies (IT), such as Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS), and Big Data, starting with business management departments down to production sites. Despite IT has reached a consistent maturity level, its integration with Operation Technology (OT) is still a mostly unresolved challenge. In this article, we propose the design of a platform that supports reliable data gathering and sharing among OT and IT layers of an industrial manufacturing company, while also putting the basis for straightforward integration of business stakeholders in the data sharing loop. By leveraging widely used open-source tools, we implemented a software prototype of the platform that Small and Medium Enterprises (SME) can use to face the IT/OT convergence challenge in an affordable way. Results collected from an extensive assessment in real manufacturing settings show that the proposed solution allows meeting both functional and non-functional requirements of a typical data gathering/sharing process in a near-real-time scenario, by granting reliability with very low overhead cost.

A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines

This manuscript presents a methodology and a practical implementation of a network architecture for industrial robot data acquisition and predictive maintenance . We propose a non-intrusive and scalable robot signal extraction architecture, easily applicable in real manufacturing assembly lines. The novelty of the paper lies in the fact that it is the first proposal of a network architecture which is specially designed to address the predictive maintenance of industrial robots in real production environments. All the infrastructure needed for the implementation of the architecture is comprised of traditional well-known industrial assets. We synchronize the data acquisition with the execution of robot routines using common Programmable Logic Controllers (PLC) to obtain comparable data batches. A network architecture that acquires comparable and structured data over time, is a crucial step to advance towards an effective predictive maintenance of these complex systems, in terms of effectively detecting time dependent degradation. We implement the architecture in a real automotive manufacturing assembly line and show the potential of the solution to detect robot joint failures in real world scenarios. The architecture is therefore specially interesting for industrial practitioners and maintenance personnel. Finally, we test the feasibility of using one-class novelty detection models for robot health status degradation assessment using data of a real robot failure. To the best of our knowledge, this is the first contribution that uses robot torque signals of a real production line failure to train one-class models. • A non-intrusive and scalable robot data acquisition architecture is proposed for real manufacturing assembly lines. • The network acquires synchronized comparable robot operational data over time. • The architecture is able to detect a significant deviation in a faulty ABB IRB 6400r robot data. • One-class novelty detection models detect deviations in real operational conditions.

Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in thepharmaceutical industry.

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discoveryresearch, requires molecular representation. Previous reports have demonstratedthat machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates andcan be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses therecent updates in AI tools as cheminformatics application in medicinal chemistry for thedata-driven decision making of drug discoveryand challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

Detecting the Causal Structure of Risk in Industrial Systems by Using Dynamic Bayesian Networks

Our study deals with detecting the causal structure of risk in industrial systems. We focus on the prioritization of risks in the form of correlated events sequences. To improve existing prioritization methods, we propose a new methodology using Dynamic Bayesian networks (DBN). We explore a new user interface for industrial control systems and data acquisition, known as Supervisory Control and Data Acquisition (SCADA), to demonstrate the analysis method of risk causal structure. Our results show that: (1) the network of variables before and after the failure is represented by a limited and distinct number of factors;(2) the network of variables before and after the failure can be graphically represented dynamically in a user interface to assist in fault prevention and diagnosis;(3) variables related to the sequence of events at the time of failure can be used as a model to predict its occurrence, and find the main cause of it, thus making it possible to prioritize the requirements of the production system on the right variables to be monitored and manage in the event of a breakdown

Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis

Considering the difficulty of data acquisition in industry, especially for failure data of large-scale equipment, classification with these class-imbalanced datasets can lead to the problems of minority categories overfitting and majority categories domination. A model-agnostic framework towards class-imbalanced fault diagnosis requirement is proposed to systematically alleviate these problems. Four sub-modules, including Time-series Data Augmentation, Data-Rebalanced sampler, Balanced Margin Loss, and classifier with Dynamic Decision Boundary Balancing are performed to improve recognition accuracy of minority categories without performance degradation on majority categories. Meanwhile, the framework is compatible with general neural networks and provides flexible model candidates to meet the need of feature extraction for different data types. Three case studies on public datasets demonstrate that proposed framework outperformed various state-of-the-art methods.

Time dependent network resource optimization in cyber–physical systems using game theory

The social and economic stability of a country is dependent on critical infrastructures (CIs) whose services range from financial to healthcare and power to transportation and communications. Most of these CIs are cyber–physical systems (CPSs), which integrate the network’s computational and communication capabilities to facilitate the monitoring and controlling of physical processes. Such systems are vulnerable to damage due to natural disasters, physical incidents, or cyber-attacks impacting the CPS organizations managing complex industrial control systems and data acquisition systems. When these CPSs are exposed to systemic cyber risks and cascaded network failures, network administrators need to recover from the compromise under limited resources. This is formulated as an attacker-defender game model to emulate the decision-making process in choosing an appropriate attack/defence mechanism in response to cybersecurity incidents using game theory. To further improve the assumptions made in the pure game-theoretic model, we relax the constraints on the rationality of the players, monetary payoff, and completeness of information by incorporating learning in games using reinforcement learning technique and compute the expected payoff using linguistic fuzzy variables.

A Versatile IoT-Approach to Process Data Acquisition

The acquisition and evaluation of process data in production engineering holds great potential. This allows detecting faulty processes at an early stage and processes to be optimized even more efficiently. However, the use of technologies for data acquisition in the manufacturing industry is far less widespread than the mentioned potential implies. This paper presents an Internet of Things approach by means of which production environments can be retrofitted easily and cost-effectively.

An Early Classification Approach for Improving Structural Rotor Fault Diagnosis

Artificial intelligence (AI)-based rotating machinery fault diagnosis has extreme importance in the industrial automation and control systems since rotating machinery constitutes approximately 40% of the overall machinery in the industry. However, the majority of AI-based solutions in rotor faults diagnosis are in an experimental stage due to: 1) inadequate and unrealistic faulty data; 2) lack of opportunities to utilize domain-specific fault features; and 3) limitations in employing deep learning and advanced learning strategies. Moreover, structural rotor fault (SRF) is one of the critical but least addressed faults in rotor faults diagnosis even though it is the root cause of the majority of rotating machinery issues. Hence, we develop an SRF diagnosis framework, which addresses the issues of industrial data acquisition by creating a subsampled data set incorporating distinctive frequency components (DFC). The data scarcity and imbalance problems are handled through an augmentation method using soft-dynamic time warping (soft-DTW), enhanced by fault information content (FIC)-based weighing scheme. At the fault classification phase, we proposed an early classification (EC) approach for SRF that predicts the faults with an acceptable tradeoff between earliness and accuracy. In this line, first, a sequential deep learning classifier is developed by considering accuracy only as an objective. Then, early decision policy is defined by taking accuracy and earliness into account. The model demonstrated exceptional performance over state-of-the-art methods in SRF diagnosis and achieved 99.5% accuracy with 14% earliness on the Meggitt testbed data set and 98.32% accuracy with 55.68% earliness on the MaFaulDa data set.

Research on Industrial Control Network Security Data Acquisition System

In this paper, based on the characteristics of industrial control network security data, research data analysis and data storage technology, design and development of industrial control network security data acquisition system, and through the AFL fuzz method for fuzzy testing, improve the system’s robustness and endogenous security performance. The realization of massive heterogeneous data collection and efficient normalization of industrial control networks can better serve the data layer and promote the rapid development of digitalization of industrial enterprises.

Status and Future of the CMS Tracker DCS

Detector Control Systems (DCS) for modern High-Energy Physics (HEP) experiments are based on complex distributed (and often redundant) hardware and software implementing real-time operational procedures meant to ensure that the detector is always in a safe state, thus maximizing the lifetime of the detector. Display, archival and often analysis of the environmental data are also part of the tasks assigned to DCS systems. The CMS Tracker Control System (TCS) is a resilient system that has been designed to safely operate the silicon tracking detector in the CMS experiment. It has been built on top of an industrial Supervisory Control and Data Acquisition (SCADA) software product WinCC OA extended with a framework developed at CERN, JCOP, along with CMS and Tracker specific components. The TCS is at present undergoing major architecture redesign which is critical to ensure efficient control of the detector and its future upgrades for the next fifteen years period. In this paper, we will present an overview of the Tracker DCS and the architecture of the software components as well as the associated deliverables.

Context-Aware Autonomous Security Assertion for Industrial IoT

The Industrial Internet of Things (IIoT) platform consists of purpose-driven communication controllers, enterprise-grade modems (routers and gateways), and edge computing systems that require integrated software and sensing capability in mission-critical environments. Extensible purpose-built industrial supervisory control and data acquisition networks are prone to numerous cybersecurity threats. In this paper, the historical databased qualitative threat assessment was part of the comprehensive risk breakdown (i.e., to quantify assessment and remediation) of the practicing industry (i.e., systems that rely on robotics, big data & analytics). Furthermore, a risk and operability (HAZOP & convolution neural-network) evaluation was proved to be the paramount study for autonomous vulnerability assessment. Through autonomous network management, continuous software monitoring, data-driven device insights, and integrated content filtering, the proposed endpoint protection scheme shows significant improvement in preventing data breaches, denial of service (DoS), and malware detection. A distinctive computational methodology to determine the cyber risk for industrial structures with IoT-explicit control factors has been programmed and elucidated in the perspective of IIoT systems. Firmware driven emulation (integrated and optimized) outcome aided to reduce breach ratio, better incident detection, and enhanced protection of confidential data.

A Blockchain Tokenizer for Industrial IOT trustless applications

The Blockchain is a novel technology with a wide range of potential industrial applications. Despite a vast range of tests, prototypes, and proof of concepts implemented in the last years, the industrial use of Blockchain technology is still in the early stages. Enabling the interaction of industrial Internet of Things (IOT) platforms with Blockchain might be challenging because standards are still missing in both these technologies. Moreover, integrating productive assets with distributed data exchange and storage technologies is a kind of activity that needs to take into account various aspects, in particular: interoperability, portability, scalability, and security that need to be guaranteed by design.This paper describes the implementation of a portable, platform-agnostic and secure Blockchain Tokenizer for Industrial IOT trustless applications. The Industrial Blockchain Tokenizer (IBT) is based on an industrial data acquisition unit able to gather data from both modern and legacy machines while also interfacing directly with sensors. Acquired data can be processed locally enabling an edge filtering paradigm and then sent to any Blockchain platform. The system has been designed, implemented and then tested on two supply chain scenarios. Tests demonstrated the system capability to act as a bridge between industrial assets and Blockchain platforms enabling the generation of immutable and trust-less “digital twins” for industrial IOT applications.

Research and Design of Wireless Communication Module Based on Single Chip Microcomputer

This paper studies a wireless communication control module with AT89S52 microcontroller as the control core. The basic principle, main circuit, hardware framework and software framework of the system are described in detail. The whole system adopts modular design, which mainly includes wireless communication control circuit between single-chip microcomputer and lower-level computer, and communication interface circuit between wireless communication module and 51 single-chip microcomputer. The communication control system communicates with the nRF2401 through the SPI of the 51 single chip microcomputer, thereby forming a connection with the lower computer through the wireless communication control module, controlling the lower machine motion controller, and saving the data received by the communication into the extended memory. The communication method of this module is simple, in addition to remote real-time control, it can also be widely used in industrial monitoring and data acquisition systems.