Industrial Monitoring Research Articles

Dielectrophoretic assembly and separation of particles and cells in continuous flow.

Dielectrophoretic (DEP) separation has been recognized as a practical tool in the separation of cells and particles for clinical diagnosis, the pharmaceutical industry and environmental monitoring. Assembly of particles and cells under DEP force is a common phenomenon and has an influence on their separation but has not been understood fully. Encouraged by these aspects, we developed a microfluidic device with a bipolar electrode array to investigate the assembly and separation of particles and cells at a large scale. First, we studied the assembly and evolution mechanisms of particles of one type under an AC electric field. Then, we investigated the interaction and assembly of multiple particles with dissimilar properties under DEP force. Depending on the development of microfluidic devices, we visualize the assembly process of yeast cells at the electrode rims and of polystyrene particles at the channel centers, and explore the influence of pearl chain formation on their separation. With increasing flow velocity from 288 to 720 μL h-1, the purity of 5 μm polystyrene particles surpasses 94.9%. Furthermore, we studied the DEP response of Scenedesmus sp. and C. vulgaris, and explored the influence of cell chains on the isolation of C. vulgaris. The purity of Scenedesmus sp. and C. vulgaris witnessed a decrease from 95.7% to 90.8% when the flow rate increased from 288 to 864 μL h-1. Finally, we investigated the extension of the electric field under chains of Oocystis sp. at the electrode rims by studying chain formation and capture of C. vulgaris, and studied its effect on cell chain length, recovered cell purity and cell concentration. When chains of Oocystis sp. were formed, the purity of C. vulgaris kept unchanged and the concentration decreased from 2793 cells per μL to 2039 cells per μL. This work demonstrates continuous DEP-based assembly and separation of particles and cells, which facilitates high-efficiency isolation of targeted cells.

Dual-mode optical ratiometric thermometry using Pr3+-doped NaSrGd(MoO4)3 phosphors with tunable sensitivity.

Owing to some special superior features, luminescence ratiometric thermometry has acquired popularity, particularly dual excitation single emission (SBR) and single excitation dual emission (FIR). Nonetheless, it remains difficult to create ratiometric thermometry that can operate in multiple modes. The integration of FIR and SBR techniques paves the way for advancements in various fields, including industrial processes, environmental monitoring, and biomedical applications, where accurate temperature measurements are crucial for optimal performance and safety. In this work, we describe a way to measure temperature based on the light-induced fluorescence of Pr3+ in NaSrGd(MoO4)3 (NSGM). The optical properties were investigated by UV-visible absorption, PL, and PLE spectroscopy. On the one hand, the emission of Pr3+ exhibits varying temperature-dependent behavior upon 450 nm excitation. Thus, a thermometer based on the FIR between the Pr3+ levels has been generated, with the highest sensitivity of approximately 0.83% K-1 over a wide temperature range of 290-440 K. Furthermore, the SBR luminescent thermometer was evaluated in the same temperature range. The effect of the Pr3+ concentration on red-emitting SBR luminescent thermometers was investigated in detail. The Sa and Sr values gradually increase, with the Pr3+ content reaching a maximum Sr value of 2.4% K-1 at 413 K for the NSGM:10% Pr3+ phosphor. These results show that Pr3+ ions have the potential to be optically active centers for luminescent thermometer applications using FIR and SBR techniques. It is anticipated that the present work will inspire other researchers to employ multi-mode optical ratiometric thermometry more widely.

Industrial batch monitoring for cell culture processes during process scale-ups: data challenges and solutions

Cell culture process scale-ups are common in biologics manufacturing as the process moves from the early stages of process development to commercial manufacturing. Statistical batch process monitoring (BPM) is frequently deployed for efficient real-time monitoring and control of cell culture processes. A BPM platform relies on historical process data to train in-control model for real-time monitoring of future batches. Deploying a BPM platform during or immediately after process scale-up is nontrivial due to limited batch campaigns at the commercial scale. In the absence of available at-scale commercial data during scale-ups, we propose an approach to reuse data sets generated at bench or pilot-scale for training BPM models at the commercial scale. The proposed method uses a scaling model to learn complex data scaling relations between bench/pilot-commercial scales. The model is then used to synthesise data sets at a commercial scale using existing in-control bench/pilot data. Using synthesised at-scale commercial scale data, a BPM model can be trained and deployed soon after the scale-up, which would not have been possible otherwise. The efficacy of the proposed approach is illustrated in an industrial cell culture process.

Vibration monitoring and automatic protection system for industrial robots

A description of the automatic protection system for an industrial robot used in milling is given. The operation of the system is based on measuring vibration parameters using vibration monitoring equipment. The developed system can be used for industrial equipment and monitoring vibration parameters. Keywords automatic protection, vibration monitoring, industrial robot, robot engineering

EM-Skin: An Artificial Robotic Skin Using Magnetic Inductance Tomography

Physical sensing by touch is essential for building intelligent artificial systems in robotic manipulation and human-robotic interaction. Inductive skins are being investigated as part of a major effort to develop the most robust and reliable touch sensors, primarily based on traditional inductive proximity sensing. Magnetic induction tomography (MIT) is an imaging system considered for medical diagnostics and industrial process monitoring. This article presents a novel electromagnetic-based skin (EM-skin) using the MIT imaging system. This is done by processing the mutual inductance data from a planar array sensing a skin-like medium, including an elastomeric medium that interfaces the MIT sensors with plates of metallic or magnetic touch elements. The paper demonstrates EM-based multi-touch, dynamical touch, and quantitative touch pressure sensing. MIT data is captured at 10 frames/s, so allowing for dynamical touch analysis. The EM-skin sensing area of 900 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> demonstrates a large area of sensing skin. The results show the successful reconstruction of dynamical sensing, where two applied cyclic touch points, with different frequencies are discriminately detected. Quantitively force sensing shows the detection of a minimum of 120 mN force, which translates to 0.38 kP of applied pressure in the described system. Further force calibration is carried out demonstrating the quantitative nature of the proposed EM skin. These results will open the way to a new generation of distributed and reliable soft skins that are versatile due to material design and processing.

A General Quality-Related Nonlinear Process Monitoring Approach Based on Input–Output Kernel PLS

Projection to latent structure (PLS) is a well-known data-based approach widely used in industrial process monitoring. Kernel PLS (KPLS) was proposed in prior studies to apply the PLS in the nonlinear process. However, KPLS-based methods only consider the nonlinear variation of the input and ignore that of the input and output simultaneously. Once the nonlinearity lies in inputs and outputs, the KPLS-based methods cannot accurately describe the nonlinear feature, and result in missing alarms. To provide a common monitoring approach for various nonlinear cases, an input–output KPLS (IO-KPLS) model is proposed. The proposed IO-KPLS maps both the original input and output variables into a high-dimensional space. A new nonlinear objective function is then established to extract latent variables. In addition, a nonlinear regression is designed to construct the IO-KPLS model. By constructing statistics, a complete quality-related process monitoring strategy is designed. Driven by the proposed strategy, the nonlinear feature between input and output can be efficiently extracted, and a comprehensive monitoring performance is provided. A numerical example and two industrial benchmarks are performed to demonstrate the efficiency of the proposed method.

Microwave Contactless Current-Sensing for Live/Dead Differentiation of Single Bioparticles on a Microfluidic Platform

The combination of microwave and microfluidic technologies enables the creation of efficient platforms to rapidly quantify live/dead bioparticles in industrial processes, biomedical research and environmental monitoring applications. In this regard, this work demonstrates the differentiation of single live/dead bioparticles with a microwave-microfluidic platform. The system is composed of a contactless current-sensing superheterodyne architecture with broadband frequency-operation bowtie electrodes. Furthermore, the 20mm × 20mm bowtie electrode has been designed and manufactured with a –10 dB frequency range, from 4GHz to 8GHz, and capable of confining the electric field in a micrometric volume. The gap of the bowtie geometry of the electrodes is adjusted to the dimensions of the 50 μm × 50 μm cross-section and 40mm long microfluidic channel. In particular, 10mL sample with 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> bacteria/mL flows through the microchannel at 19.8 μL/min, focused with elasto-inertial effect enhanced with 500 ppm of polyethylene oxide. Accordingly, from the signal captured by the electrodes, the energy variation of the applied electric field produced by the bioparticles is sensed by a current-based transimpedance amplifier. Here, the incorporation of the transimpedance amplifier in the measurement system improves the SNR by 4 dB. In addition, the superheterodyne transceiver has been optimized in terms of local oscillator power and bandwidth. Furthermore, the microfluidic system has been adjusted so that the balance of hydrodynamic forces confines bioparticles at the centerline of the microchannel within less than 5 μm. As a result, the system measures the cytoplasmic content of live and dead flowing bacteria with a difference of 6 dB and relative deviation of 3.2%.

Causal Generative Model for Root-Cause Diagnosis and Fault Propagation Analysis in Industrial Processes

Fault tracing technology, including root-cause diagnosis and propagation analysis, has become a growing hot spot in the field of industrial process monitoring. However, it is currently limited by the use of restricted alarm sequence data and the analysis without fault propagation analysis. To solve these problems, this article proposes a novel fault tracing method, namely causal topology-based variable-wise generative model (CTVGM). The CTVGM is first established according to the topological order of the variable causal graph. It contains a series of causal functions that are trained with normal data. Then, fault samples can be restored by the CTVGM to build up a diagnosis index called the recovery ratio (RR), which is used to determine the root causes. Meanwhile, the fault propagation paths are inferred by the recovery routes. In addition, a hierarchical CTVGM-based fault tracing strategy is designed to reduce the computation burden and enhance the modeling efficiency for large-scale complicated processes. The effectiveness of the proposed fault tracing method is verified on a numerical example and the Tennessee Eastman process case. Compared with existing methods, the results show that the proposed method not only achieves more accurate root-cause diagnosis performance but also obtains fault tracing results that are highly consistent with the process mechanisms.

Industry 4.0 energy monitoring system for multiple production machines

Energy consumption is a key contributor to scope two emissions and to the cost of manufacturing. Reducing energy consumption in manufacturing can significantly improve environmental performance by reducing the scope two carbon footprint for industries, especially before the de-carbonization of energy. Monitoring energy consumption for manufacturing facilities, multiple machines, and production resources is essential to achieve this. Hence, this study focuses on developing an energy monitoring system that allows the industry to monitor real-time energy consumption for multiple machines via an open-sources application platform. The implementation of this system is presented in detail, including the integration, and interlinking of Internet of Things (IoT) devices and web application platforms. The development of databases, web application platform, and data visualisation is reported. This energy-monitoring platform displays real-time data, including voltage, current, peak power, energy consumption, energy cost, and scope 2 carbon emissions, empowering end users to make decisions according to manufacturing objectives. This study contributes to the industrial monitoring of energy patterns, which is crucial to facilitate energy efficiency measures under industry 4.0 competency. The implementation of this system does not require modifications to machines or equipment, reducing the barriers to implementation in the industry. Therefore, this monitoring system is necessary for moving towards net zero carbon and sustainable industry 4.0 manufacturing.

A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks

The quantum leap in mobile data traffic and high density of wireless electronic devices, coupled with the advancements in industrial radio monitoring and autonomous systems, have created great challenges for smooth wireless network operations. The fifth-generation and beyond (B5G) (also being referred to as sixth-generation (6G)) wireless communication technologies, due to their compatibility with the previous generations, are expected to overcome these unparalleled challenges. Accompanied by traditional and new techniques, the massive multiple input multiple output (mMIMO) approach is one of the evolving technologies for B5G/6G systems used to control the ever-increasing user stipulations and the emergence of new cases efficiently. However, the major challenges in deploying mMIMO systems are their high computational intricacy and high computing time latencies, as well as difficulties in fully exploiting the multi-antenna multi-frequency channels. Therefore, to optimize the current and B5G/6G wireless network elements proficiently, the use of the mMIMO approach in a HetNet structure with artificial intelligence (AI) techniques, e.g., machine learning (ML), distributed learning, federated learning, deep learning, and neural networks, has been considered as the prospective efficient solution. This work analyzes the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks. To provide a complete insight into the mMIMO systems with emerging antenna and propagation precoding techniques, we address and identify various relevant topics in each section that may help to make the future wireless systems robust. Overall, this work is designed to guide all B5G/6G stakeholders, including researchers and operators, aiming to understand the functional behavior and associated techniques to make such systems more agile for future communication purposes.

Drone Based Inspection of Above Ground Storage Tanks. A Multi Criteria Evaluation Approach

Unmanned Aerial Vehicles (UAVs) is an emergent technology with the potential to drastically change the landscape of many civil and industrial activities in the near future. A promising field of application is the inspection and monitoring of industrial plants, where UAVs can be a valid alternative to traditional approaches with a better cost-effectiveness and a higher safety level. Although the disruptive innovative potential of this technology is generally recognized, a consistent methodological approach towards the evaluation of the drawbacks and benefits of employing UAVs in critical industrial operations can hardly be found in the current scientific literature. In such context, this paper proposes a framework for assessing the effectiveness of UAV technology in industrial monitoring applications within a structured multi-objective framework. In particular, the paper compares the traditional ground-based inspection services of Above Ground Storage Tank (AST) with UAV based inspection, by means of the well-known Analytic Hierarchy Process (AHP) method considering time, cost and safety criteria. A case study is also proposed to demonstrate the effectiveness of the approach proposed.

Fluid-induced vibration evolution mechanism of multiphase free sink vortex and the multi-source vibration sensing method

The multiphase free sink vortex (MFSV) has frequently appeared in practical industry processes, including the refinement of steel stream, hydroelectric energy conversion of the tidal power station, and draining state detection of nuclear power equipment. Its internal real-time monitoring is crucial for enhancing product yield, raising energy utilization, and achieving safe and high-effective production. However, harsh industrial conditions, such as high temperature, reactivity disturbance, and high-frequency shock, prevent the direct detection of the MFSV flow field. Given the above issues, this paper proposed a novel fluid–structure coupling modeling and sensing method for the MFSV-induced vibration mechanism. A fluid–structure coupling mechanic model based on the Rankine vortex model and Helmholtz equation is built to determine critical penetration conditions. Then, a Flügge equation-based solving approach is proposed to obtain the vibration evolution mechanism. Forced displacement response and vibration characters are discussed with the multi-source vibration sensing methods (MSVSM). Finally, a wavelet packet (WP) transform-based vibration sensing technique is presented to identify the distortion attribute in the critical transition state. The results show that the proposed modeling and solution method oriented to the MFSV-induced vibration have better revealed the vibration evolution mechanism and its displacement response. Flow flux dominates the critical transition time and vibration intensities of the MFSV flow field; the WP transform-based sensing method can effectively identify transient distortion attributes. The relevant result can offer a helpful reference for fluid-induced vibration detection and provide technical solutions for industrial monitoring systems.

A mixture of probabilistic predictable feature analysis for multi-mode dynamic process monitoring

In modern industrial processes, the demand for safety and reliability is increasingly prioritized, spawning quantities of related research on process monitoring models. It is significant for process monitoring to consider multi-mode operating conditions simultaneously. This work proposes an efficient method based on multi-mode probabilistic predictable feature analysis (MPPFA) for complex industrial process monitoring. A strategy combining deep auto-encoder (DAE) and Gaussian mixture model (GMM) is first designed to extract low-dimensional features and identify possible running modes. In line with the obtained features, original training samples are classified into corresponding Gaussian distributions with reasonable probabilities. Further, local PPFA models are established based on the separated training batches. Five monitoring statistics, including TP2, SPEP, and DIP for PPFAs, TD2 and SPED for DAE, are defined to detect abnormalities. The proposed method is first applied to a three-phase flow facility, and its superiority over comparison methods has been verified. The effectiveness of the proposed method is further demonstrated through an experiment on a practical coal pulverising system.

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

AbstractRecently, stacked autoencoder (SAE)‐based deep learning has been widely employed for industrial process fault detection, which can extract representative low‐dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self‐reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC‐SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC‐SAE can describe the distribution characteristics of process data, and more informative high‐level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T‐squared () and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC‐SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed‐batch fermentation penicillin (FBFP) process.

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications.

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements' presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.

IOT‐HML: A hybrid machine learning technique for IoT enabled industrial monitoring and control system

SummaryIndustrial 4.0 makes manufacturers more vulnerable to current challenges and makes it easier to adapt to market changes. It is essential to focus on monitoring and controlling the production system before complex accidents occur. To overcome above research gaps, we shift to industrial 4.0, which combine IoT and mechanism learning for industrial monitor and manage. Here, we propose a hybrid machine learning technique for IoT enabled industrial monitoring and control system (IoT‐HML). The main goal of the research is to overcome the issues of information security and control systems by developing a hybrid machine learning technique. Compared to the existing AODV protocol, the proposed C‐IWO based routing protocol outperformed efficiently in terms of 19.2% average delay, 12.7% average energy consumption, 10.26% average throughput, 3.8% average delivery ratio, and 16.33% average loss ratio, respectively. In addition, the accuracy 98.5%, sensitivity 97.3%, specificity 98.2%, precision 98.35%, recall 98.32%, and F‐measure 97.49% of proposed CP‐LNN technique is very high compare to obtainable state‐of‐art classifiers.

Characterization of H2S QEPAS detection in methane-based gas leaks dispersed into environment

The increase in fatal accidents and chronic illnesses caused by hydrogen sulfide (H2S) exposure occurring in various workplaces is pushing the development of sensing systems for continuous and in-field monitoring of this hazardous gas. We report here on the design and realization of a Near-IR quartz-enhanced photoacoustic sensor (QEPAS) for H2S leaks detection. H2S QEPAS signal was measured in matrixes containing up to 1 % of methane (CH4) and nitrogen (N2) which were chosen as the laboratory model environment for leakages from oil and gas wells or various industrial processes where H2S and CH4 can leak simultaneously. An investigation of the influence of CH4 on H2S relaxation and photoacoustic generation was proposed in this work and the sensor performances were carefully assessed with respect to CH4 content in the mixture. We demonstrated the high selectivity, with no cross talk between H2S, H2O and CH4 absorption lines, high sensitivity, and fast response time of the developed sensor, achieving a minimum detection limit (MDL) of 2.5 ppm for H2S with 2 s lock-in integration time. The employed 2.6 µm laser allowed us to employ the sensor also for CH4 detection, achieving an MDL of 85 ppm. The realized QEPAS sensor lends itself to the development of a portable and compact device for industrial monitoring.

Enhancing the Modbus Communication Protocol to Minimize Acquisition Times Based on an STM32-Embedded Device

The primary function of a distributed bus is to connect sensors, actuators, and control units that are used for an acquisition process. Application domains, such as industrial monitoring and control systems, manufacturing processes, or building automation, present different requirements that are not exactly invariable and coherent. Updating data from Modbus-type devices involves updating data through a technique called polling, which involves repeatedly scanning the registers from each device. This paper highlights the performance of Modbus communication, considering scenarios in which distributed devices are integrated and accessed registers are or are not at consecutive addresses. The Modbus protocol allows reading one or more holding-type data registers. If the registers are not at consecutive addresses, multiple requests are required, with implications for the real-time characteristics of the data acquisition system. We studied the data update times within the SMARTConvert application when variable numbers of registers are accessed, and we designed an extension for the Modbus protocol. The major reason Modbus is used in current research is that no assumptions are required about application semantics, and the performance/resource ratio for generic services is excellent.

Reproduction, stock status and fishery for walleye pollock in Peter the Great Bay in the modern period

The aim of the study is to analyze the current state of reproduction, resources and pollock fishery in Peter the Great Bay.Result: It is shown that the increase in pollock abundance is associated with the appearance of a strong yearclass of 2014 in Peter the Great Bay. Pollock biomass has increased significantly, mass spawning has resumed in traditional spawning areas, commercial catch of pollock increased to record levels. However, during the three spawning seasons of the year-class 2014 there was no significant recruitment, so the fish stock is expected to return to a low level.Novelty: The results of scientific research conducted in the Peter the Great Bay in 2019–2022 made it possible to obtain new data on the reproduction, current state and structure of pollock stock.Practical significance: The obtained estimates of the current state and structure of pollock stock will be used for short-term and long-term forecasts.Research methods: The data of research bottom trawl and ichthyoplankton surveys and pollock catch statistics from the Industry Monitoring System (OSM «Fishing») were used.

Moisture measurement of tea leaves during withering using multifrequency microwave signals optimized by ant colony optimization

The rapid and accurate moisture content (MC) measuring of withering tea leaves faces multiple challenges. In this study, a self-developed microwave sensing system equipped with multifrequency-swept signals (2.00–10.00 GHz, an interval of 10.00 MHz) was used to measure the MC of withering tea leaves (16.25%–77.65%). Four kinds of hybrid-based ant colony optimizations were advanced to optimize microwave frequency signals, and the attenuation features at six frequencies (2.16, 3.42, 3.88, 4.48, 5.32 and 7.70 GHz) and phase shift features at four frequencies (2.84, 4.04, 7.88 and 8.43 GHz) were selected. Based on those optimized features, three primary models (support vector regression, extreme gradient boosting, and multi-layer perception) and one secondary model (decision tree) presented the best MC values (the average of mean absolute error was 0.349). The results showed the potential application of microwave technology in the MC monitoring in tea processing industry. • A microwave sensing system was developed for moisture detection in tea withering. • A hybrid-based ACO algorithm was designed to optimize the frequency features. • Attenuation and phase shift features correlated with MC were selected by MRACO. • The best MC detection was achieved by the stacking model of SVR, LGBM, and MLP.