Advanced feedforward control techniques: Comprehensive review and a real-time industrial application

Task mapping and flow priority assignment of real-time industrial applications for network-on-chip based design

Driver behavior analysis is one of the critical issues that need to be addressed to prevent traffic accidents. It contributes to many real-time applications, such as usage-based pricing (UBI), pay-as-you-drive (PAY-D), and insurance premium calculations. Driver Behavior Profiling-Prognosis (DBP-P) is considered a quantitative risk assessment parameter in road accidents and is a fusion of two sub-processes, behavior scoring and classification of driving patterns. The selection of features like speed or acceleration is the essential and decisive factor in automobile driving behaviour. Though there exists a number of such schemes in the literature, most of them primarily focus on independently on each vehicle and score them. This goal, however, does not clearly indicate any driver's driving quality or its risk of collision with other vehicles. Therefore, to overcome the limitations of the literature, this paper proposes a relative, adaptive, and distributed driver behaviour profiling technique, named Distributed Adaptive Recommendation & Time-stamp based Estimation of Driver-Behaviour (DARTED), to generate driving scores to quantify and classify driver behavior as good or bad. Moreover, the driver scores can be computed at each timestamp with a classified label that can be used in various applications aiming for collision analysis. The experimental results indicate that the proposed method achieves significant accuracy in different traffic scenarios. The model may be helpful to researchers for study and enhance understanding and many real-time industrial applications.

Traditional machine learning methods assume that training and testing data must be from the same machine running condition (MRC) and drawn from the same distribution. However, in several real-time industrial applications, this assumption does not hold. The traditional methods work satisfactorily in steady-state conditions but fail in time-varying conditions. In order to utilize time-varying data in variable MRCs, this article proposes a novel low-level knowledge transfer framework using a deep neural network (DNN) model for condition monitoring of machines in variable running conditions. The low-level features have been extracted in time, frequency, and time–frequency domains. These features are extracted from the source data to train the DNN. The trained DNN-based parameters are then transferred to another DNN, which is modified according to the low-level features extracted from the target data. The proposed approach is validated through three case studies on: 1) the air compressor acoustic data set; 2) the Case Western Reserve University bearing data set; and 3) the intelligent maintenance system bearing data set. The prediction accuracy obtained for the above case studies is as high as 100%, 93.07%, and 100%, respectively, with fivefold cross-validation. These real-time results show considerable improvement in the prediction performance using the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Condition-based monitoring schemes are widely applicable to rotating machines in various industries since they operate in tough working situations, and consequently, unpredicted failures occur. These unpredicted failures may cause perilous accidents in the industries. CBM systems prevent such failures, which results in the reduction of equipment damage and, hence, increases machinery lifetime. Modern industries are so complex and generating huge data, and these data can be collected using sensors, but placing a large number of sensors is difficult and expensive for different but similar kinds of faults in industries. This also increases the cost due to additional sensors and circuits. In this article, the authors have proposed a novel low-level knowledge transfer framework using the deep neural network (DNN)-based method for condition monitoring of machines in variable running conditions. Low-level features have been extracted to reduce the computations of DNN drastically with improved performance. This article also considered additional faults in the target domain, which is more practical in real-time applications. The proposed scheme has been validated with three case studies on acoustic and vibration signatures.

Creating Rich Human-machine Interfaces with Rational Rhapsody and Qt for Industrial Multi-core Real-time Applications

Real-time Industrial applications in the scope of Industry 4.0. present significant challenges from the communication perspective: low latency, ultra-reliability, and determinism. Given that wireless networks provide a significant cost reduction, lower deployment time, and free movement of the wireless nodes, wireless solutions have attracted the industry attention. However, industrial networks are mostly built by wired means because state-of-the-art wireless networks cannot cope with the industrial applications requirements. In this article, we present the hardware implementation of wireless SHARP (w-SHARP), a promising wireless technology for real-time industrial applications. w-SHARP follows the principles of time-sensitive networking and provides time synchronization, time-aware scheduling with bounded latency and high reliability. The implementation has been carried out on a field-programmable gate array-based software-defined radio platform. We demonstrate, through a hardware testbed, that w-SHARP is able to provide ultra-low control cycles, low latency, and high reliability. This implementation may open new perspectives in the implementation of high-performance industrial wireless networks, as both PHY and MAC layers are now subject to be optimized for specific industrial applications.

This chapter presents some numerical techniques for designing proportional-integral derivative algorithm control, which is considered to be the classical control algorithm in automatic systems engineering, and the polynomial R (regulation), S (sensitivity) and T (tracking) (RST) control using a more flexible digital approach with two degrees of freedom, which is easy to integrate in real-time industrial applications. It illustrates the digital structure of the closed loop system. The methods based on pole placement are used in the design of closed-loop systems, which ensure a high level of performance in driving industrial processes. The chapter also presents the technique of predictive control, which is often used in advanced digital control systems. It discusses the key ideas behind its design and the purpose of this approach, highlights the various algorithmic steps of predictive control, and describes the most representative methods (“dead beat”) for predictions in the range of a sample period.

Modern fieldbus communication architectures for real-time industrial applications

Hybrid architectures, based on combinations of analogic, symbolic, and neural methods, are well suited for real-time applications in advanced robotics. Real-time industrial applications are mainly based on the correction of preplanned programs. So far, the planning and control modules of these kind of applications are often unable to react and/or classify un-expected events. The approach described attempts to integrate the sensor-based analogic method and the neural method into a multiple-level architecture that operates on an analogic world model, so that the action planning can be performed in a smart, reactive way. Given the task, the system builds the world model of the scenario. The reasoning and planning modules act both at the strategic as well as reactive levels, and the activated sensor-based motor strategies handle the sensorial data inputs and drive the robot controller module in the execution of the stream of motor commands. The interaction between the different levels is mainly based on the idea of maintaining and updating in real-time the world model, so that each module can locally operate on specific parts of the whole world model. >

Wireless sensor networks (WSN) are networks for gathering data from sensor nodes that have been applied in industry for a long time. In real-time industrial applications with tight latencies, schedulability is one of the most critical issues. Some authors have proposed centralized scheduling algorithms for time-slotted channel hopping (TSCH) networks for real-time applications in industry, however, they have some disadvantages such as schedulability and high data traffic. In this paper, we improve the schedulability, latency, data traffic by dynamically prioritizing packets which are based on number duplex-conflicts and dynamically combining the packets. As a result, we show that the packet-combining algorithm improves schedulability and minimizes the amount of traffic in a network when compared with existing approaches.

In image processing, pattern recognition, and computer vision, one of the most powerful techniques for feature extraction is to use moments. Real-time applications of this method, however, have been prohibited due to the intensive computation encountered in calculating the moments. One solution to this problem is to adopt specially designed hardware accelerators. This paper describes, from a practical standpoint, the design of a custom hardware accelerator for speeding up the moment computation. The design of the core functional units and the design of the overall system based on a wavefront array architecture are discussed. The moment accelerator can be easily configured into different sizes to meet diverse application requirements cost effectively. Testing results based on implementation using field-programmable gate array devices show that, at an affordable cost, the proposed hardware accelerator can deliver real-time speeds for moment computation. Elimination of this computational bottleneck makes it possible to use moments-based features in real-time industrial applications.

Chapter 9 - Pharmaceutical application of extremozymes

Induction motors are widely used in several industrial sectors. However, the dimensioning of induction motors is often inaccurate because, in most cases, the load behavior in the shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for dimensioning induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since the proposed approach uses current, voltage and speed values as the only input parameters, one of its potentialities is related to the facility of hardware implementation for industrial environments and field applications. Simulation results are also presented to validate the proposed approach.

Industrial construction remains a major driver of global energy consumption and resource depletion: the need to improve ‘energy efficiency’ in this sector goes beyond mere cost reduction, positioning itself as a fundamental element of sustainable economic growth and environmental stability – hence the need for a methodological paradigm shift in optimising energy use in industrial infrastructures. This research aims to develop an integrated framework for improving energy efficiency in industrial construction by synthesising advanced predictive modelling, ‘multi-parameter evaluation metrics’ and adaptive artificial intelligence-based energy management systems – thus creating a scalable and transferable methodology. A combination of empirical data analysis, computational modelling and statistical validation was used – key parameters such as ‘heat transfer coefficients’, ‘building envelope efficiency’ and ‘energy redistribution patterns’ were estimated in real-time industrial applications; regression models and correlation matrices were applied to check whether observed trends were consistent with theoretical benchmarks. The integration of ‘artificial intelligence-assisted energy distribution algorithms’ demonstrated unprecedented energy optimisation – heat loss reductions of up to 27% were achieved in high-intensity production areas, and overall energy efficiency improvements reached 19,6% in several case studies; a cost-benefit analysis confirmed an average payback period of 4,8 years, confirming the practicality of implementation at different industrial scales. The proposed methodological approach goes beyond direct industrial applications – its adaptability to related technology sectors, including ‘high-efficiency manufacturing’ and ‘large-scale infrastructure projects’, highlights its transdisciplinary potential; empirical validation of AI optimised energy saving strategies offers a replicable model for integration into national and international regulatory frameworks, ensuring compliance with stringent environmental standards while maintaining economics The study highlights the need for continuous improvement of algorithms – future work should aim to create ‘self-optimising systems’ capable of real-time reconfiguration in response to fluctuations in energy demand, bridging the gap between theoretical efficiency modelling and evolving industrial conditions.

Many industrial real-time applications in various domains, e.g., automotive, industrial automation, industrial IoT, and industry 4.0, require ultra-low end-to-end network latency, often in the order of 10 milliseconds or less. The IEEE 802.1 time-sensitive networking (TSN) is a set of standards that supports the required low-latency wired communication with ultra-low jitter. The flexibility of such a wired connection can be increased if it is integrated with a mobile wireless network. The fifth generation of cellular networks (5G) is capable of supporting the required levels of network latency with the Ultra-Reliable Low Latency Communication (URLLC) service. To fully utilize the potential of these two technologies (TSN and 5G) in industrial applications, seamless integration of the TSN wired-based network with the 5G wireless-based network is needed. In this article, we provide a comprehensive and well-structured snapshot of the existing research on TSN-5G integration. In this regard, we present the planning, execution, and analysis results of the systematic review. We also identify the trends, technical characteristics, and potential gaps in the state of the art, thus highlighting future research directions in the integration of TSN and 5G communication technologies. We notice that 73% of the primary studies address the time synchronization in the integration of TSN and 5G technologies, introducing approaches with an accuracy starting from the levels of hundred nanoseconds to one microsecond. Majority of primary studies aim at optimizing communication latency in their approach, which is a key quality attribute in automotive and industrial automation applications today.

One of the major challenges in industrial process control is to deal with nonlinearities in the plants. There have been a significant amount of research efforts towards design and development of appropriate, reliable and promising control techniques to deployed on real-time industrial applications. Some of the widely used and acknowledged control methods lack in terms of tuning unknown system parameters. The reason is their unadaptive or fixed nature. This paves the field well for adaptive controllers. Their biggest advantage is their automatic updation of unknown system paramters, that saves quite some resources and manpower and ensures an overall stable control strategy. In this regard, system modeling happens to be the prime and pertinent task so that it can set the basis for a stable control law synthesis. This reserach work proposes a polynomial adaptive model recently introduced called U-Model to be used for online system identification of Chemical Process Plant. U-Model is a simple, stable and reliable which has previously yielded encouraging results when applied to various application in different scenario. The aforementioned plant shall be used for investigation on its Flow process. The modeling results shall be compared and validated by other commonly known and utilized modeling structures.