AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

This article presents a novel approach to the acquisition, processing, and analytics of industrial food production by employing state-of-the-art artificial intelligence (AI) at the edge. Intelligent Industrial Internet of Things (IIoT) devices are used to gather relevant production parameters of industrial equipment and motors, such as vibration, temperature and current using built-in and external sensors. Machine learning (ML) is applied to measurements of the key parameters of motors and equipment. It runs on edge devices that aggregate sensor data using Bluetooth, LoRaWAN, and Wi-Fi communication protocols. ML is embedded across the edge continuum, powering IIoT devices with anomaly detectors, classifiers, predictors, and neural networks. The ML workflows are automated, allowing them to be easily integrated with more complex production flows for predictive maintenance (PdM). The approach proposes a decentralized ML solution for industrial applications, reducing bandwidth consumption and latency while increasing privacy and data security. The system allows for the continuous monitoring of parameters and is designed to identify potential breakdown situations and alert users to prevent damage, reduce maintenance costs and increase productivity.publishedVersio

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.publishedVersio

Automation, Control and Modeling of Compound Semiconductor Thin-Film Growth

This report documents the results of a laboratory-directed research and development (LDRD) project on control and agile manufacturing in the critical metalorganic chemical vapor deposition (MOCVD) and molecular beam epitaxy (MBE) materials growth processes essential to high-speed microelectronics and optoelectronic components. This effort is founded on a modular and configurable process automation system that serves as a backbone allowing integration of process-specific models and sensors. We have developed and integrated MOCVD- and MBE-specific models in this system, and demonstrated the effectiveness of sensor-based feedback control in improving the accuracy and reproducibility of semiconductor heterostructures. In addition, within this framework we have constructed ''virtual reactor'' models for growth processes, with the goal of greatly shortening the epitaxial growth process development cycle

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data

Variation in volatile organic compounds in Atlantic salmon mucus is associated with resistance to salmon lice infection

Salmon lice are ectoparasites that threaten wild and farmed salmonids. Artificial selection of salmon for resistance to the infectious copepodid lice stage currently relies on in vivo challenge trials on thousands of salmon a year. We challenged 5750 salmon with salmon lice (Lepeophtheirus salmonis) from two distinct farmed strains of salmon in two separate trials. We found that volatile organic compounds (VOC), 1-penten-3-ol, 1-octen-3-ol and 6-methyl-5-hepten-2-one in the mucus of the salmon host after salmon lice infection, were significantly associated with lice infection numbers across a range of water temperatures (5 °C, 10 °C, 17 °C). Some VOCs (benzene, 1-octen-3-ol and 3,5,5-trimethyl-2-hexene) were significantly different between lines divergently selected for salmon lice resistance. In a combined population assessment, selected VOCs varied between families in the range of 47- 59% indicating a genetic component and were positively correlated to the salmon hosts estimated breeding values 0.59–0.74. Mucosal VOC phenotypes could supplement current breeding practices and have the potential to be a more direct and ethical proxy for salmon lice resistance provided they can be measured prior to lice infestation

Variation in volatile organic compounds in Atlantic salmon mucus is associated with resistance to salmon lice infection

Salmon lice are ectoparasites that threaten wild and farmed salmonids. Artificial selection of salmon for resistance to the infectious copepodid lice stage currently relies on in vivo challenge trials on thousands of salmon a year. We challenged 5750 salmon with salmon lice (Lepeophtheirus salmonis) from two distinct farmed strains of salmon in two separate trials. We found that volatile organic compounds (VOC), 1-penten-3-ol, 1-octen-3-ol and 6-methyl-5-hepten-2-one in the mucus of the salmon host after salmon lice infection, were significantly associated with lice infection numbers across a range of water temperatures (5 °C, 10 °C, 17 °C). Some VOCs (benzene, 1-octen-3-ol and 3,5,5-trimethyl-2-hexene) were significantly different between lines divergently selected for salmon lice resistance. In a combined population assessment, selected VOCs varied between families in the range of 47- 59% indicating a genetic component and were positively correlated to the salmon hosts estimated breeding values 0.59–0.74. Mucosal VOC phenotypes could supplement current breeding practices and have the potential to be a more direct and ethical proxy for salmon lice resistance provided they can be measured prior to lice infestation.publishedVersio

Mechanisms of Atmospheric Copper Sulfidation and Evaluation of Parallel Experimentation Techniques

A physics-based understanding of material aging mechanisms helps to increase reliability when predicting the lifetime of mechanical and electrical components. This report examines in detail the mechanisms of atmospheric copper sulfidation and evaluates new methods of parallel experimentation for high-throughput corrosion analysis. Often our knowledge of aging mechanisms is limited because coupled chemical reactions and physical processes are involved that depend on complex interactions with the environment and component functionality. Atmospheric corrosion is one of the most complex aging phenomena and it has profound consequences for the nation's economy and safety. Therefore, copper sulfidation was used as a test-case to examine the utility of parallel experimentation. Through the use of parallel and conventional experimentation, we measured: (1) the sulfidation rate as a function of humidity, light, temperature and O{sub 2} concentration; (2) the primary moving species in solid state transport; (3) the diffusivity of Cu vacancies through Cu{sub 2}S; (4) the sulfidation activation energies as a function of relative humidity (RH); (5) the sulfidation induction times at low humidities; and (6) the effect of light on the sulfidation rate. Also, the importance of various sulfidation mechanisms was determined as a function of RH and sulfide thickness. Different models for sulfidation-reactor geometries and the sulfidation reaction process are presented

Room Temperature Continuous Wave InGaSsN Quantum Well Vertical Cavity Lasers Emitting at 1.3 um

Selectively oxidized vertical cavity lasers emitting at 1294 nm using InGaAsN quantum wells are reported for the first time which operate continuous wave at and above room temperature. The lasers employ two n-type Al{sub 0.94}Ga{sub 0.06}As/GaAs distributed Bragg reflectors each with a selectively oxidized current aperture adjacent to the optical cavity, and the top output mirror contains a tunnel junction to inject holes into the active region. Continuous wave single mode lasing is observed up to 55 C. These lasers exhibit the longest wavelength reported to date for vertical cavity surface emitting lasers grown on GaAs substrates