ALBERT

Description: An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

Description: Rail inspections are required and used to ensure safety and preserve the availability of railway infrastructure. According to the statistics published by railroad administrations worldwide, the transverse fissure appearing in railhead is the principal cause of rail accidents. These particular defects are initiated inside the railhead. Detection of these cracks has always been challenging because a defect signature remains mostly small until the defect size reaches a significant value. The present work deals with the theoretical analysis of an integrated contact-less system for rail diagnosis, which is based on ultrasounds. The generation of these waves was performed through non-ablative laser sources. Rotational laser vibrometry was used to achieve the reception of the echoes. Detection of flaws in the rail was monitored by considering special ultrasound wave signal based indicators. Finite element modeling of the rail system was performed, and transverse defect detection of the rail was analyzed.

Description: A Linear Fresnel Reflector (LFR) is a recent technology with good potential in small-scale solar power applications. It is composed of many long rows of mirrors that focus the sunlight onto a fixed elevated tubular receiver. Mirror segments are aligned horizontally and track the sun such that the receiver does not need to be moved. The efficiency with which the LFR can convert solar to thermal energy depends on the accuracy of the sun tracking system. To maximize the degree of sunlight capture, precise solar tracking is needed so that incident solar rays can be adequately focused to the focal point given by the location of the tubular receiver. The tilt angles of each row are relevant for the tracking controller to achieve correct positioning. Encoders are generally employed in closed-loop tracking systems as feedback signals used to inform the controller with the actual position of collector mirrors. Recently, inclinometers have begun to replace encoders as the most viable and cost-effective sensor technology solution; they offer simpler and more precise feedback, as they measure the angle of tilt with respect to gravity and provide the ability to adjust the system to the optimal angle for maximum output. This paper presents the research results on the development of remote measurements for the precise control of an LFR tracking system, by using distributed angle measurements. The applied methodology enables precision measurement LFR inclination angles through the fusion of data from multiple accelerometers, supported by low-cost wireless transceivers in a wireless sensor network, capable of exchanging information in a cloud infrastructure.

Description: Advanced ceramics are widely used in industry due to their unique properties. However, the machining of ceramic components by conventional methods is difficult due to their high level of hardness and brittleness. In this sense, laser beam machining (LBM) is presented as an alternative to conventional methods, enabling the machining of workpieces through more accurate and less invasive techniques. Despite the advantages of laser machining, the process still needs to be studied in detail, as advanced ceramic machining is considered a stochastic process. Thus, real-time monitoring systems are required in order to optimize the ceramic laser machining. Therefore, this paper proposes a novel method for monitoring the cutting kerf in the laser cutting process of ceramic components using a low-cost piezoelectric transducer (PZT) and digital signal processing. Tests were performed on the surface of an alumina ceramic workpiece under different machining conditions. The cutting kerf was measured by a digital microscope and the raw signals from the PZT transducer were collected at a sampling rate of 2 MHz. Time domain and frequency domain analyses were performed in order to find a frequency band that best correlates with the process conditions. Finally, a linear regression was calculated in order to correlate the PZT signal and the measured kerf. The results showed that the piezoelectric transducer was sensitive to the acoustic activity generated during the process, allowing the real-time monitoring of the cutting kerf. Thus, the approach proposed in this paper can be used efficiently in the monitoring of the laser cutting process.

Description: Crack initiation and propagation vary the mechanical properties of the asphalt pavement and further alter its designate function. As such, this paper describes a numerical study of a multi-layered strain sensor for the structural health monitoring (SHM) of asphalt pavement. The core of the sensor is an H-shaped Araldite GY-6010 epoxy-based structure with a set of polyvinylidene difluoride (PVDF) piezoelectric transducers in its center beam, which serve as a sensing unit, and a polyurethane foam layer at its external surface which serves as a thermal insulation layer. Sensors are coated with a thin layer of urethane casting resin to prevent the sensor from being corroded by moisture. As a proof-of-concept study, a numerical model is created in COMSOL Multiphysics to simulate the sensor-pavement interaction, in order to design the strain sensor for SHM of asphalt pavement. The results reveal that the optimum thickness of the middle polyurethane foam is 11 mm, with a ratio of the center beam/wing length of 3.2. The simulated results not only validate the feasibility of using the strain sensor for SHM (traffic load monitoring and damage detection), but also to optimize design geometry to increase the sensor sensitivity.

Description: Recently, Lidl had to set a recall action due to dangerous pieces of plastic found in the cheese products. The plastic shards, if swallowed, can cut the oral cavity or obstruct breathing. Current inspection techniques in the cheese industry are for the detection of metals using X-ray that does not offer a complete solution, as many foreign bodies can go undetected. This paper demonstrates the use of a portable real-time microwave sensing technique for the nondestructive detection of plastic in cheese. The electromagnetic (EM) patch antenna was designed and tested on five Cheddar cheese samples. Different sizes of plastic shards, 1 × 10 mm, 2 × 15 mm and 5 × 20 mm, were inserted into the samples, and measurements were taken with and without foreign objects. The initial results demonstrated that the patch antenna at 4GHz was able to detect and classify Polyvinyl Chloride (PVC) shards with an R2 = 0.95. The initial results are promising, and further investigation will be undertaken to detect different shapes and types of foreign objects in food products.

Description: Data collection plays an important role in pavement health monitoring, which is usually performed using costly devices, including point-based lasers and laser scanners. The main aim of this study measures pavement characteristics using an RGB-D sensor. By recording the depth and color images simultaneously, the sensor benefits the data fusion. By mounting the sensor on a moving cart, and fixing the vertical distance from the ground, data were collected along 100 m of the asphalt pavement using MATLAB. At each stop point, multiple frames were collected, the central region of interests was stored, and a low pass filter was subsequently applied to the data. To create a 3D surface of the pavement, sensor calibration was performed to map the RGB and depth infrared images. The SURF (speeded-up robust features) and MSAC (M-estimator sample consensus) algorithms were used to match the stitched images along the longitudinal profile. A case study of measuring roughness and rutting is applied to test the validity of the method. The result confirms that the proposed system is capable of measuring such indices with acceptable accuracy.

Description: Human-robot collaboration combines the extended capabilities of humans and robots to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, real-time motion tracking is necessary to identify if the human worker body parts enter the restricted working space solely dedicated to the robot. Tracking these motions using decentralized and different tracking systems requires a generic model controller and consistent motion exchanging formats. In this work, our task is to investigate a concept for a unified real-time motion tracking for human-robot collaboration. In this regard, a low cost and game-based motion tracking system, e.g., HTC Vive, is utilized to capture human motion by mapping into a digital human model in the Unity3D environment. In this context, the human model is described using a biomechanical model that comprises joint segments defined by position and orientation. Concerning robot motion tracking, a unified robot description format is used to describe the kinematic trees. Finally, a concept of assembly operation that involves snap joining is simulated to analyze the performance of the system in real-time capability. The distribution of joint variables in spatial-space and time-space is analyzed. The results suggest that real-time tracking in human-robot collaborative assembly environments can be considered to maximize the safety of the human worker. However, the accuracy and reliability of the system regarding system disturbances need to be justified.

Description: The current technological trends associated with Industry 4.0 and the Internet of Things (IoT) require an interconnected network of sensor nodes providing distributed information on the environment in order to enable intelligent action to be taken by control systems. Typical examples are the condition monitoring of machines or industrial equipment, or the detection of hazardous environmental conditions (e.g., in chemical plants). Such sensors need to be distributed in areas that are difficult to reach for wiring or to exchange batteries, and thus need to be self-powered and energy-independent. In this work, we provide an overview of possible strategies to realise a positive energy balance in autonomous sensor nodes without the use of batteries, focussing on gas sensors for air-quality monitoring as a use case. We will first present ways to reduce the power budget of sensing elements using self-heating nanowires made of CMOS-compatible metal oxides. We will then concentrate on energy harvesting and storage, showing state-of-the-art possibilities in both cases: broadband piezoelectric harvesters, perovskite-based photovoltaic elements, and high-energy density ceramic capacitors. Finally, we will discuss the possibility of integrating all sensor node elements in a single device using advanced interconnect technologies.

Description: Wind energy is one of the most promising forms of renewable energy. For the constant evolution of power generation technology, the use of sensors is fundamental to the development of wind turbine emulators. A wind turbine emulator allows tests and evaluations of a wind power system, regardless of weather conditions. Therefore, to further improve this technology, this work focuses on the application of a torque transducer and a rotary encoder for the implementation of a closed-loop wind turbine emulator. The sensors provide the torque and speed feedback signals to the computational model so that the model could plot the power curves and produce the set point voltage used by a variable-frequency drive (VFD) to control a three-phase induction motor (TIM). The emulator was implemented using a control algorithm designed on LabVIEW, with an NI 6211 for the data acquisition. Finally, the system emulates the behaviour of a wind turbine, considering the variations in wind speed, aerodynamic phenomena, load effects, and pitch angle. Experimental results demonstrated the effectiveness of using the TIM-VFD assembly for emulating a wind turbine since the wind turbine emulator behaved like a wind turbine in real-time.