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Controlling machine degradation enhances the accuracy of the remaining-useful-life estimation and offers the ability to control failure type and time. In order to achieve optimal degradation control, the system controller must be cognizant of the consequences of its actions by considering the degradation each action imposes on the system. This article presents a method for designing cost-aware controllers for linear systems, to increase system reliability and availability through degradation control. The proposed framework enables learning independent of the system's physical structure and working conditions, enabling controllers to choose actions that reduce system degradation while increasing system lifetime. To this end, the cost of each controller's action is calculated based on its effect on the state of health. A mathematical structure is proposed, to incorporate these costs into the cost function of the linear-quadratic controller, allowing for optimal feedback for degradation control. A simulation validates the proposed method, demonstrating that the optimal-control method based on the proposed cost function outperforms the linear-quadratic regulator in several ways.

Controlling the machine’s state of health (SoH) increases the accuracy of the remaining useful life estimation and enables the control of the failure time by keeping the system operational until the desired maintenance time is reached. To achieve system reliability through SoH control, the system controller must consider the impact of its actions on other parameters, such as degradation. This article proposes a structure for designing degradation-aware controllers for systems with available physical models. A system using this approach can learn autonomously, irrespective of the system’s physical structure and degradation model, and opt for control actions that enhance the system’s reliability and availability. To this end, first, a method is proposed to compute the cost associated with the actions taken by the controller. Second, a new cost function is introduced that incorporates the costs associated with degradation into the cost function utilized in model predictive control. In the third step, dynamic programming and deterministic scheduling are used to calculate the optimal action based on the defined cost function. Finally, the proposed control method is validated through simulation, demonstrating its ability to effectively manage machine degradation and achieve optimal performance according to production and maintenance plans.

Construction and manufacturing workers undertake physically laborious activities which put them at risk of developing serious musculoskeletal disorders (MSDs). In the EU, millions of workers are being affected by workplace-related MSDs, inflicting huge financial implications on the European economy. Besides that, increased health problems and financial losses, severe shortages of skilled labor also emerge. The work aims to create awareness and accelerate the adoption of exoskeletons among SMEs and construction workers to reduce MSDs. Large-scale manufacturers and automobile assemblers are more open to adopt exoskeletons, however, the use of exoskeletons in small and medium enterprises (SMEs) is still not recognized. This paper presents an experimental study demonstrating the advantages of different exoskeletons while performing workers’ tasks. The study illustrates how the use of certain upper and lower body exoskeletons can reduce muscle effort. The muscle activity of the participants was measured using EMG sensors and was compared while performing designated tasks. It was found that up to 60% reduction in human effort can be achieved while performing the same tasks using exoskeletons. This can also help ill workers in rehabilitation and putting them back to work. The study concludes with pragmatic recommendations for future exoskeletons.

This study aims to investigate the deployment and maintenance of Digital Twin (DT) in an industrial ring rolling manufacturing process. Three parameters were analyzed (i) data cleaning and processing, (ii) DT development and updating, and (iii) secure communication. A process-based DT model was developed using iba-ag and Python, prioritizing security throughout the process. The study underscores the significance of secure DT system, with particular attention to the challenges of real-time updates affected by process operations and environmental factors. This emphasizes the adaptability of the DT while acknowledging the expertise of process operators. The findings promote a deeper understanding of DT implementation, particularly for process developers, maintenance personnel, and operators. The research highlights the importance of security in DT systems, especially when dealing with industrial data influenced by production and environmental factors. Therefore, by focusing on security and deployment in an open-source environment, this study contributes to the practical use of DT systems for predictive maintenance and process optimization within the industrial settings.

Efficient production planning hinges on reducing costs and maintaining output quality, with machine degradation management as a key factor. The traditional approaches to control this degradation face two main challenges: high costs associated with physical modeling and a lack of physical interpretability in machine learning methods. Addressing these issues, our study presents an innovative solution focused on controlling the degradation, a common cause of machine failure. We propose a method that integrates machine degradation as a virtual state within the system model, utilizing relevance vector machine-based identification designed in a way that offers physical interpretability. This integration maximizes the machine’s operational lifespan. Our approach merges a physical machine model with a physically interpretable data-driven degradation model, effectively tackling the challenges in physical degradation modeling and accessibility to the system disturbance model. By embedding degradation into the system’s state-space model, we simplify implementation and address stability issues. The results demonstrate that our method effectively controls degradation and significantly increases the machine’s mean time to failure. This represents a significant advancement in production planning, offering a cost-effective and interpretable method for managing machine degradation.

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise's influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach's superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air

Communications in the mmWave spectrum are gaining relevance in the last years as they are a promising candidate to cope with the increasing demand of throughput and latency in different use cases. Nowadays several efforts have been carried out to characterize the propagation medium of these signals with the aim of designing their corresponding communication protocols accordingly, and a wide variety of both outdoor/indoor locations have already been studied. However, very few works endorse industrial scenarios, which are particularly demanding due to their stringent requirements in terms of reliability, determinism and latency. This work aims to provide an insight of the propagation of 60 GHz mmWave signals in a typical industrial workshop in order to explore the particularities of this kind of scenario. In order to achieve this, an extensive measurement campaign has been carried out in this environment and a stochastic channel model has been proposed and validated.

Diagnosis, fault prediction, and Remaining Useful Life (RUL) estimation are among the predictive maintenance research subjects used for maintenance cost reduction. Using the available data with different machine learning methods, especially deep learning methods, the accuracy of estimation and prediction of faults and RUL have increased dramatically. However, due to the statistical nature of the machine learning methods and the limitations of available datasets, physically interpreting this information might be impossible. On the other hand, controlling the degradation and faults in the machines as the optimum predictive maintenance solution needs the physical interpretation of the method's outcome. In order to test the new process-based methods for degradation and fault control, datasets with more information are required (compared to available datasets). In this article, we introduce an open-source degradation simulator for linear systems. This simulator can simulate the degradation in closed-loop machines whose dynamics are known. It is also possible to simulate different degradation models for different system parts simultaneously by adding different processes and output noise to the system. This simulator can generate enough data to test new machine learning-based predictive maintenance methods.

This paper presents an experimental study demonstrating the advantages of different exoskeletons while performing workers' tasks. The study illustrates that how the use of certain upper and lower body exoskeletons can reduce the muscle effort around the shoulder, hip, and knee joints. Therefore, overhead, shoulder level, virtual chair position, and deep squatting position drilling tasks were performed, and muscle activity was recorded using EMG sensors. The muscle activity of the participants was compared with and without wearing exoskeletons while performing the same tasks. It was found that up to 60% reduction in the human effort can be achieved while performing the same tasks using exoskeletons. Many of the large-scale automobile manufacturers have already adopted exoskeletons in their assembly lines, however, the use of exoskeletons in small and medium enterprises (SMEs) is still not recognized. Therefore, this study aims to create awareness and accelerate adoption of exoskeletons among SMEs and construction workers.

Communication systems are a key element for the industry 4.0 revolution, where the remote access to the machinery is a fundamental part for the automation of tasks related to monitoring and control of the different industrial processes. There is an increasing interest in performing such communications using a wireless medium, as they offer several advantages as a lower cost, greater flexibility or the ability to operate in moving elements. However, existing works have showed that the achievable performance in the sub-6 GHz frequency bands is insufficient to cope with all the requirements, which motivated the analysis of the millimeter wave spectrum for these use cases. Industrial environments present a harsh condition for electromagnetic wave propagation, where the abundance of reflective surfaces can present difficulties to properly exchange information. Thus, a thorough analysis and characterization of the propagation through this kind of environment is necessary to develop protocols and standards accordingly. This work provides the results of measurements carried out in two industrial facilities, which are a university workshop and a pit oven building from a steel company. Metrics of the results are computed and discussed as well, where a significantly larger losses can be seen for the pit oven measurements compared to other industrial scenarios.

Predicting the failure and estimating the machine's state of health is information that supports the production planning and maintenance management systems to increase productivity and reduce maintenance and downtime costs. However, controlling the degradation in the machines will improve the system's reliability and resilience and make high-level decisions more accurate and reliable. To control the degradation in the machines, time should be included in the cost function as a variable, which alters the markovian properties of the system dynamic. In this article, we include the degradation cost in the quadratic cost function of the infinite horizon controller and calculate the optimal feedback according to the dynamics of the degradation using dynamic programming. It will be shown that the infinite horizon control will convert to the finite horizon, and the controller will be able to control the degradation according to the desired degradation at the desired time. In the end, with the help of simulation, we show that the degradation controller can control the degradation in the MIMO systems.

The robotic wearable exoskeletons have been developed due to various advantages offered by these devices. These advantages are manifested by integrating the human and a robot into one system under the user's control, which motivated researchers to develop different exoskeletons through years. However, several advances in exoskeleton; still, dealing with the various technical challenges in designing these impressive devices is inevitably. This paper aims to introduce informative resources and quick guidance of various technical challenges as such information is critical for exoskeleton development. The constructive discussion is intended to encourage researchers, innovators and academia to be aware of these challenges. Finally, the contemporary research gaps with various challenges have been highlighted, which remain to be solved as well as some future directions in this field that will have far-reaching effects on developing exoskeletons.

Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.

Recently, due to their abundant benefits, current-mode instrumentation amplifiers have received considerable attention in medical instrumentation and read-out circuit for biosensors. This paper is focused on the design of current-mode instrumentation amplifiers for portable, implantable, and wearable electrocardiography and electroencephalography applications. To this end, a CMOS differential voltage second-generation current conveyor (DVCCII) based on a linear transconductor is presented. A new band-pass instrumentation amplifier, based on the designed DVCCII, is also implemented in this paper. The concept of the proposed differential voltage current conveyor and instrumentation amplifier is validated numerically and their predicted performance is presented. The simulation results of the presented circuits were tested for 0.18 µm TSMC CMOS technology in a post layout simulation level using the Cadence Virtuoso tool with a ±0.9 V power supply, and demonstrated that the designed DVCCII has a wide dynamic range of ±400 mV and ±0.85 mA and a power consumption of 148 µW. The layout of the DVCCII circuit occupies a total area of 0.378 µm2 . It is shown that the designed DVCCII benefits from good linearity over a wide range of input signals and provides a low input impedance at terminal X. Two versions of the proposed band-pass instrumentation amplifier using pseudo resistances were designed with different specifications for two different applications, namely for EEG and ECG signals. Numerical analyses of both designs show proper outputs and frequency responses by eliminating the undesired artifact and DC component of the EEG and ECG input signals.

Millimeter wave (mmWave) frequency bands, ranging from 24 GHz to 100 GHz, are primarily considered for the low latency communication in fifth generation mobile technology (5G). This article investigates path loss modeling atthe 24 GHz industrial, scientific, and medical (ISM) band in indoor industrial environments. Radio channel measurements were carried out to study the narrow-band channel characteristics in three different industrial environments in Sweden. The measurements were conducted using an affordable but highly competent in-house assembled mmWave testbed, reusing several radio instruments available in our lab. A comprehensive study isperformed to investigate the path loss based on three different models.

Gait disorders, muscle weakness, spinal cord injuries (SCIs) and other work-related disorders have increased the need for rehabilitation exoskeletons—specifically, for the hip because a huge percentage of mechanical power comes from the hip joint. However, realising a lightweight rehabilitation hip exoskeleton for mobility and at-home use with reliable control is challenging. The devices developed are restricted by a joint actuator and energy source design and tend to have various uncertainties. Thus, this study tested the robustness of four optimal controller cases in a simulation-based environment. We sought to determine whether the most robust optimal controller consumed less energy and demonstrated better performance in tracking the desired signal. The robustness of the optimal cases was tested with the hip torque signals of healthy subjects. The number of sit-to-stand (STS) instances and the walking distance at various speeds were calculated. The results showed that the most robust case controller was more energy efficient for STS, but not for walking activity. Furthermore, this study provides compelling evidence that various optimal controllers have different degrees of robustness and effects on energy consumption.

Millimeter wave (mmWave) wireless technology is primarily considered for low latency communication in fifth-generation mobile technology (5G) and has the potential to revolutionize industrial automation and manufacturing processes. This article investigates multipath radio propagation in indoor industrial environments at the 24 GHz industrial, scientific and medical (ISM) mmWave frequency band. The wideband radio channel measurements were carried out in four different industrial environments in Sweden. The measurements were conducted using an affordable but highly competent in-house assembled mmWave testbed, reusing radio instruments available in our lab. The measurement environments were chosen based on their radio wave reflection characteristics. The multipath propagation characteristics are analyzed with respect to the power delay profile (PDP), coherence bandwidth, and root mean square (RMS) delay spread. Additionally, the Saleh-Valenzuela model parameters are estimated for these industrial environments.

Almost the half of mechanical energy of a human gait cycle is generated by a hip joint. Therefore, this paper discusses a rehabilitation hip exoskeleton, starting with the modeling, simulation and ending with controlling its hip joints. A MATLAB based simulation environment with the use of Simscape Multibody toolbox was utilized to design and control the robotic hip exoskeleton. More details of adding a series elastic actuators (SEA) to the hip joints with the mathematical model are presented. However, the linearised mathematical model of the entire exoskeleton was found in simulation software which establish the basic need of controlling hip joints. The trajectory tracking is commonly used of controlling rehabilitation exoskeleton and to ensure a safe and reliable motion tracking methods, two desired torque signals were tested and analysed with the optimal linear quadratic regulator (LQR). The experiments with two torque signals–representing the sit-to-stand (STS) and the walking activity, demonstrated good performance of the motion gait tracking based on torque signals of a healthy person, which is carried out in the simulation environment. Furthermore, some studies in a human robot interaction are also mentioned in this paper.

The availability of wireless networked control systems (WNCSs) has increased the interest in controlling multi-agent systems. Multiple feedback loops are closed over a shared communication network in such systems. An event triggering algorithm can significantly reduce network usage compared to the time triggering algorithm in WNCSs, however, the control performance is insecure in an industrial environment with a high probability of the packet dropping. This paper presents the design of a distributed event triggering algorithm in the state feedback controller for multi-agent systems, whose dynamics are subjected to the external interaction of other agents and under a random single packet drop scenario. Distributed event-based state estimation methods were applied in this work for designing a new event triggering algorithm for multi-agent systems while retaining satisfactory control performance, even in a high probability of packet drop condition. Simulation results for a multi-agent application show the main benefits and suitability of the proposed event triggering algorithm for multi-agent feedback control in WNCSs with packet drop imperfection.

Coronavirus caused pandemics as many viruses did through human history. The current pandemic causes overwhelmed healthcare system, locked down cities, and massive fatality among humans. Thus, different robots have been used since the COVID outbreak worldwide to reduce spreading infectious diseases and support frontline healthcare workers. This paper sets out the different robots implemented for hospital, non-hospital use, and possible use that can be deployed amidst the pandemic. A literature survey of versatile robots during COVID-19 is introduced. Roboticists contributed with wheeled and drone robots with various applications to assist medical care systems and society during the ongoing crisis. Pandemics are common throughout human history and difficult to avoid or prevent; thus, we intend to encourage societies, academia, engineers and innovators to invest more in robots that cannot catch the virus and consequently introduce beneficial solutions to fight such pandemic in the future.

This paper investigates the distributed event-triggered control of leader-follower consensus problem with harsh environmental conditions such as a high probability of burst packet drops occurs in the communication channels between the vehicles. The main objective in the vehicular platoons is to ensure that all follower vehicles are traveling at the same speed as the leader while maintaining a desired inter-vehicular distance with considering the minimum communication resource usage. To achieve the object, a novel distributed event-triggered communication strategy based on the estimation and prediction of self-states and other agents’ states in each vehicle is proposed which includes two parallel algorithms. The first one is based on comparing the state estimation and prediction of the vehicle itself, and the second one is based on comparing the state estimation and prediction of other vehicles which have interaction in their process with the active vehicle. Collision avoidance, speed convergence, and limited communication resource usage of each vehicle are simultaneously considered in the design of the event-triggered mechanism in a harsh packet drops environment. Numerical simulations are presented to show that the proposed event triggering algorithm under three scenarios of burst packet drops can converge the consensus problem of vehicular platooning.

In industrial production, it is of great importance to have high availability in its production equipment. Well-functioning maintenance is a significant factor for a high level of availability. This can be achieved by minimizing the number of reactive maintenance stops and optimizing scheduled maintenance. New methods for predictive maintenance provide a good opportunity for this, but most technologies that are available today are designed for individual sub-systems and they are rarely designed for a complex, interconnected machine. In the process industry, raw materials are rocessed into a finished product in a continuous flow through several subsystems and if one subsystem stops, the entire process flow stops. For these processes, it is more important to optimize the maintenance efforts for subsystems so maintenance can take place synchronized. This paper describes a method of supervised control that includes maintenance aspects; health parameters indicating deterioration are included in a MIMO controller. The method is verified in a simulation of a rolling mill with three rollers. The results show that it is possible to optimize the whole complex process including several subprocesses by using a health parameter as a control parameter and broadening the controllability of the system by dividing the workload in a way that all the subsystems reach the desired degradation level for maintenance in a desired optimum time., The research project is financed by the European Commission within the European Regional Development Fund, the SwedishAgency for Economic and Regional Growth, Region Gävleborg, and the University of Gävle.

This article investigates the wide-band channel characteristics at 24 GHz ISM band in a mobile radio environment. The mobility in the test environment is achieved by attaching the transmit antenna to a KUKA robot's arm. The radio measurements were carried out inside the robotics lab at the University of Gävle, Sweden. The radio channel measurements were carried out at various situations, e.g., line of sight (LOS), non-line of sight (NLOS), regular lab environment, reflective environment, and different velocities of the robot's arm. The influence of these situations on the power delay profile, Doppler spectral density, root mean square (RMS) delay spread, RMS Doppler spread, coherence bandwidth and coherence time, has been studied., QC 20210205

In this research paper, fusion in the air (FIA) and fusion at the receiver (FAR) - two different approaches of multi-hypotheses distributed detection for wireless sensor networks with decision fusion center (DFC) - are investigated. The DFC is equipped with multiple antennas, whereas each of the sensors has a single antenna. The performance of these schemes is evaluated in two different scenarios; with identical sensors and non-identical sensors, in terms of their detection capabilities. For a global event, identical sensors observe an equal number of hypotheses, whereas the number of hypotheses detected by the non-identical sensors can be different. When all the sensors in the network are identical, the FIA based technique has a higher detection probability in transmit power constrained situations. However, the FAR scheme performs better when the transmit power budget is higher. Additionally, in the network with non-identical wireless sensors, the FAR based technique is unable to exploit the benefits from the local decisions of the low capability sensors. Therefore the FAR scheme has a lower detection probability than the FIA based approach.

In this paper, a multi-hypothesis distributed detection technique with non-identical local detectors is investigated. Here, for a global event, some of the sensors/detectors can observe the whole set of hypotheses, whereas the remaining sensors can either see only some aspects of the global event or infer more than one hypothesis as a single hypothesis. Another possible option is that different sensors provide complementary information. The local decisions are sent over a multiple access radio channel so that the data fusion is formed in the air before reaching the decision fusion center (DFC). An optimal energy fusion rule is formulated by considering the radio channel effects and the reliability of the sensors together, and a closed-form solution is derived. A receive beamforming algorithm, based on a modification of Lozano’s algorithm, is proposed to equalize the channel gains from different sensors. Sensors with limited detection capabilities are found to boost the overall system performance when they are used along with fully capable sensors. The additional transmit power used by these sensors is compensated by the designed fusion rule and the antenna array gain. Additionally, the DFC, equipped with a large antenna array, can reduce the overall transmit energy consumption without sacrificing the detection performance., QC 20220405

There has been very little exploration when it comes to design distributed detection techniques and data fusion rules with non-identical sensors. This concept can be utilized in many possible applications within industrial automation, surveillance and safety. Here, for a global event, some of the sensors/detectors in the network can observe the full set of the hypotheses, whereas the remaining sensors infer more than one hypotheses as a single hypothesis. The local decisions are sent to the decision fusion center (DFC) over a multiple access wireless channel. In this paper, a fusion rule based on minimization of variance of the local mis-detection is proposed. The presence of sensors with limited detection capabilities is found to have a positive impact on the overall system performance, both in terms of probability of detection and transmit power consumption. Additionally, when the DFC is equipped with a large antenna array,the overall transmit power consumption can be reduced without sacrificing the detection performance., QC 20191108

Smart maintenance strategies are becoming increasingly important in the industry, and can contribute to environmentally and economically sustainable production. In this paper a recently developed latent variable framework for nonlinear-system identification is considered for use in smart maintenance. A model is first identified using data from a system operating under normal conditions. Then the identified model is used to detect when the system begins to deviate from normal behavior. Furthermore, for systems that operate on separate batches (units), we develop a new method that identifies individual models for each batch. This can be used both to detect anomalous batches and changes in the system behavior. Finally, the two methods are evaluated on two different industrial case studies. In the first, the purpose is to detect fouling in a heat exchanger. In the second, the goal is to detect when the tool in a wood moulder machine should be changed.

Human-robot interaction (HRI) is movingtowards the human-robot synchronization challenge. Inrobots like exoskeletons, this challenge translates to thereliable motion segmentation problem using wearabledevices. Therefore, our paper explores the possibility ofsegmenting the motion reversals of a rigid-IMU clusterusing screw-based invariants. Moreover, we evaluate thereliability of this framework with regard to the sensorplacement, speed and type of motion. Overall, our resultsshow that the screw-based invariants can reliably segmentthe motion reversals of a rigid-IMU cluster., QC 20190111

Human-robot interaction (HRI) is moving towards the human-robot synchronization challenge. In robots like exoskeletons, this challenge translates to the reliable motion segmentation problem using wearable devices. Therefore, our paper explores the possibility of segmenting the motion reversals of a rigid-IMU cluster using screw-based invariants. Moreover, we evaluate the reliability of this framework with regard to the sensor placement, speed and type of motion. Overall, our results show that the screw-based invariants can reliably segment the motion reversals of a rigid-IMU cluster., Funding agencies:- Royal Melbourne Institute of Technology- Active and Assisted Living programme at KTH, FACT