Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three datasets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared on multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure

Outcome Measures in Facioscapulohumeral Muscular Dystrophy Clinical Trials

Facioscapulohumeral muscular dystrophy (FSHD) is a debilitating muscular dystrophy with a variable age of onset, severity, and progression. While there is still no cure for this disease, progress towards FSHD therapies has accelerated since the underlying mechanism of epigenetic derepression of the double homeobox 4 (DUX4) gene leading to skeletal muscle toxicity was identified. This has facilitated the rapid development of novel therapies to target DUX4 expression and downstream dysregulation that cause muscle degeneration. These discoveries and pre-clinical translational studies have opened new avenues for therapies that await evaluation in clinical trials. As the field anticipates more FSHD trials, the need has grown for more reliable and quantifiable outcome measures of muscle function, both for early phase and phase II and III trials. Advanced tools that facilitate longitudinal clinical assessment will greatly improve the potential of trials to identify therapeutics that successfully ameliorate disease progression or permit muscle functional recovery. Here, we discuss current and emerging FSHD outcome measures and the challenges that investigators may experience in applying such measures to FSHD clinical trial design and implementation

Generating a Super-resolution Radar Angular Spectrum Using Physiological Component Analysis

In this study, we propose a method for generating an angular spectrum using array radar and physiological component analysis. We develop physiological component analysis to separate radar echoes from multiple body positions, where echoes are phase-modulated by propagating pulse waves. Assuming that the pulse wave displacements at multiple body positions are constant multiples of a time-shifted waveform, the method estimates echoes using a simplified mathematical model. We exploit the mainlobe and nulls of the directional patterns of the physiological component analysis to form an angular spectrum. We applied the proposed method to simulated data to demonstrate that it can generate a super-resolution angular spectrum

Radar-based Measurement of Pulse Wave using Fast Physiological Component Analysis

2022 International Workshop on Antenna Technology (iWAT), 16-18 May 2022, Dublin, IrelandThis study proposes a fast blind signal separation technique for human arterial pulse wave propagation measurement. One of the authors previously developed a blind signal separation method called physiological component analysis that uses mathematical modeling of the measured physiological signals, including the pulse wave propagation, and this method improves the signal separation accuracy when applied to array signal processing. Physiological component analysis, however, is known to require long computation times because it is based on high-dimensional global optimization. In this paper, we propose a method to reduce the dimensionality of the decision variables for the optimization process that uses the Schelkunoff polynomial method. Using this dimension reduction technique, we propose a new algorithm, called fast physiological component analysis, and the performance of this algorithm is evaluated using numerical simulations

Compact differential-fed planar filtering antennas

This paper proposes novel low-profile differential-fed planar antennas with embedded sharp frequency selectively. The antennas are compact and easy to integrate with differential devices without matching baluns. The antenna design is formulated as a topology optimization problem, where requirements on impedance bandwidth, directivity, and filtering are used as the design objectives. The optimized antennas operate over the frequency band 6.0-8.5 GHz. The antennas have reflection coefficients below -15 dB, cross-polarization levels below -42 dB, a maximum gain of 6.0 ± 0.5 dB, and a uniform directivity over more than 130° beamwidth angle in the frequency band of interest. In addition, the antennas exhibit sharp roll-off between the operational band and frequencies around the 5.8GHz WiFi band and the 10 GHz X-band. One antenna has been fabricated with a good match between simulation and measurement results. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Wireless readout method for resonant sensors based on instantaneous frequency measurement

Drahtloses Erfassen verschiedener physikalischer Größen, wie Temperatur, Kraft und Drehmoment, ist bereits heute eine wichtige Aufgabe der industriellen Messtechnik und wird durch die fortschreitende Automatisierung auch in anderen Gebieten stetig relevanter. Einen vielversprechenden Ansatz stellen dafür resonante Hochfrequenz-Sensoren dar, beispielsweise basierend auf akustischen Oberflächenwellen (SAW-Sensoren). Diese rein passiven Sensoren sind extrem robust und können auch unter schwierigsten Umgebungsbedingungen eingesetzt werden. Bisher scheitern jedoch viele Anwendungen dieser zukunftsträchtigen Technologie an den aufwendigen und teuren Lesegeräten, die zum Abfragen der Sensoren benötigt werden. Die vorliegende Arbeit stellt deshalb ein neues drahtloses Ausleseverfahren für resonante Sensoren vor, bei dem erstmalig das Prinzip der instantanen Frequenzmessung (Augenblicksfrequenz- bzw. Momentanfrequenzmessung) verwendet wurde, um das Antwortsignal des Sensors auszuwerten. Durch diesen interferometrischen Ansatz lassen sich hohe Messwertaktualisierungsraten mit deutlich reduziertem Hardwareaufwand realisieren. Um Nichtidealitäten sowie Einflüsse von Temperatur und Alterungseffekte der analogen Hardwarekomponenten zu minimieren, wurde eine In-situ-Linearisierung verwendet, die bekannte Referenzsignale einspeist, um damit systematische Fehler in den nachfolgenden unbekannten Messungen digital zu kompensieren. Ein Kernpunkt dieser Arbeit stellt die detaillierte theoretische Untersuchung zur Systemauslegung sowie den Systemgrenzen und Fehlerkompensationsmöglichkeiten des vorgeschlagenen Verfahrens dar. Dazu wurden alle Einzelkomponenten inklusive möglicher Quereinflüsse im Gesamtsystemkontext evaluiert und neben internen Fehlerquellen auch externe Störeinflüsse betrachtet. Dabei wurden entscheidende neue Erkenntnisse für den praktischen Einsatz dieses innovativen Messkonzeptes erlangt, die sich zum Entwurf eines optimierten Gesamtsystems nutzen lassen. Um die Realisierbarkeit des Konzepts zu zeigen, wurden Laboruntersuchungen durchgeführt und ein Systemdemonstrator im 2,4-GHz-Frequenzband entworfen und evaluiert. Dieser konnte mit einfacher Schaltungstechnik eine 3-Sigma-Präzision der Frequenzmessung unter 2 Millionstel (ppm) bei 1000 Messungen pro Sekunde erreichen. Zusammen mit der unkomplizierten Signalverarbeitung werden dadurch neue Maßstäbe hinsichtlich kostengünstiger Realisierungsmöglichkeiten gesetzt und enormes Potential für verschiedene industrielle, automotive und medizinische Anwendungen demonstriert.Wireless acquisition of various physical quantities, such as temperature, force and torque, is already an important task in industrial metrology and is becoming more and more relevant in other areas as a result of the increasing automation. Resonant high-frequency sensors, for example based on surface acoustic waves (SAW sensors), are a promising approach. These purely passive sensors are extremely robust and can be used even under the most difficult environmental conditions. So far, however, many applications of this innovative technology cannot be realized due to the complex and expensive reading devices required to interrogate the sensors. The present work therefore presents a new wireless readout method for resonant high-frequency sensors, using the concept of instantaneous frequency measurement for the first time to evaluate the response signal of the sensor. This interferometric approach allows high measurement update rates with significantly reduced hardware requirements. In order to minimize non-idealities as well as influences of temperature and aging effects of the analog hardware components, an in-situ linearization was used which feeds known reference signals to digitally compensate systematic errors in the following unknown measurements. A core topic of this work is the detailed theoretical investigation of the system design as well as the system limits and error compensation possibilities of the proposed method. For this purpose, all individual components including possible cross-influences were evaluated in the overall system context and, besides internal error sources, external interferences were also considered. Thereby decisive new findings for the practical application of this innovative measuring concept were obtained which can be used for the design of an optimized overall system. In order to demonstrate the feasibility of the presented concept, laboratory investigations were performed and a system demonstrator in the 2.4 GHz frequency band was designed and evaluated. Using simple circuit technology, the system demonstrator was able to achieve a 3 sigma precision of the frequency measurement of less than 2 parts per million (ppm) at 1000 measurements per second. Together with the low complexity signal processing, this sets new standards in terms of cost-effective implementation possibilities and demonstrates huge potential for various industrial, automotive and medical applications

Design of microstrip patch antenna to deploy unmanned aerial vehicle as UE in 5G wireless network

The use of unmanned aerial vehicle (UAV) has been increasing rapidly in the civilian and military applications, because of UAV's high-performance communication with ground clients, especially for its intrinsic properties such as adaptive altitude, mobility, and flexibility. UAV deployment can be monitored and controlled through 5G wireless network as user equipment (UE) along with other devices. A highly directive microstrip patch antenna (MPA) could establish long-distance communication by overcoming air attenuation and reduce co-channel interference in the limited region if UAV uses a specifically dedicated band, which might enhance spatially reuse of the spectrum. Also, MPA is highly recommended for UAV because of its low weight, low cost, compact size, and flat shape. In this paper, we have designed a highly directive single-band 2×2 and 4×4 antenna array for 5.8 GHz and 28 GHz frequency respectively for UAV application in a focus to deploy UAV through 5G wireless network. Here, The Roger RT5880 (lossy) material utilize as a substrate due to its lower dielectric constant which achieves higher directivity and good mechanical stability. Inset feed technique used to feed antenna for lowering input impedance which provides higher antenna efficiency. The results show a wider bandwidth of 702 MHz and 1.596 GHz for 5.8 GHz and 28 GHz antenna array correspondingly with a compact size

Potential key challenges for terahertz communication systems

The vision of 6G communications is an improved performance of the data rate and latency limitations and permit ubiquitous connectivity. In addition, 6G communications will adopt a novel strategy. Terahertz (THz) waves will characterize 6G networks, due to 6G will integrate terrestrial wireless mobile communication, geostationary and medium and low orbit satellite communication and short distance direct communication technologies, as well as integrate communication, computing, and navigation. This study discusses the key challenges of THz waves, including path losses which is considered the main challenge; transceiver architectures and THz signal generators; environment of THz with network architecture and 3D communications; finally, Safety and health issues

Experimental Demonstration of Accurate Noncontact Measurement of Arterial Pulse Wave Displacements Using 79-GHz Array Radar

In this study, we present a quantitative evaluation of the accuracy of simultaneous array-radar-based measurements of the displacements caused at two parts of the human body by arterial pulse wave propagation. To establish the feasibility of accurate radar-based noncontact measurement of this pulse wave propagation, we perform experiments with four participants using a 79-GHz millimeter-wave ultra-wideband multiple-input multiple-output array radar system and a pair of laser displacement sensors. We evaluate the accuracy of the pulse wave propagation measurements by comparing the displacement waveforms that are measured using the radar system with the corresponding waveforms that are measured using the laser sensors. In addition, to evaluate the estimates of the pulse wave propagation channels, we compare the impulse response functions that are calculated from the displacement waveforms obtained from both the radar data and the laser data. The displacement waveforms and the impulse responses both demonstrated the good agreement between the results of the radar and laser measurements. The normalized correlation coefficient between the impulse responses obtained from the radar and laser data on average was as high as 0.97 for the four participants. The results presented here strongly support the feasibility of accurate radar-based noncontact measurement of arterial pulse wave propagation