sensor signal
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2022 ◽  
Vol 167 ◽  
pp. 108765
Author(s):  
Zixiao Yang ◽  
Peng Xu ◽  
Biao Zhang ◽  
Chuanlong Xu ◽  
Liming Zhang ◽  
...  

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>


2021 ◽  
Author(s):  
◽  
Sunethra Pitawala

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shashvat Prakash ◽  
Antoni Brzoska

Component failures in complex systems are often expensive. The loss of operation time is compounded by the costs of emergency repairs, excess labor, and compensation to aggrieved customers. Prognostic health management presents a viable option when the failure onset is observable and the mitigation plan actionable. As data-driven approaches become more favorable, success has been measured in many ways, from the basic outcomes, i.e. costs justify the prognostic, to the more nuanced detection tests. Prognostic models, likewise, run the gamut from purely physics-based to statistically inferred. Preserving some physics has merit as that is the source of justification for removing a fully functioning component. However, the method for evaluating competing strategies and optimizing for performance has been inconsistent. One common approach relies on the binary classifier construct, which compares two prediction states (alert or no alert) with two actual states (failure or no failure). A model alert is a positive; true positives are followed by actual failures and false positives are not. False negatives are when failures occur without any alert, and true negatives complete the table, indicating no alert and no failure. Derivatives of the binary classifier include concepts like precision, i.e. the ratio of alerts which are true positives, and recall, the ratio of events which are preceded by an alert. Both precision and recall are useful in determining whether an alert can be trusted (precision) or how many failures it can catch (recall).  Other analyses recognize the fact that the underlying sensor signal is continuous, so the alerts will change along with the threshold. For instance, a threshold that is more extreme will result in fewer alerts and therefore more precision at the cost of some recall. These types of tradeoff studies have produced the receiver operating characteristic (ROC) curve. A few ambiguities persist when we apply the binary classifier construct to continuous signals. First, there is no time axis. When does an alert transition from prescriptive to low-value or nuisance? Second, there is no consideration of the nascent information contained in the underlying continuous signal. Instead, it is reduced to alerts via a discriminate threshold. Fundamentally, prognostic health management is the detection of precursors. Failures which can be prognosticated are necessarily a result of wear-out modes. Whether the wear out is detectable and trackable is a system observability issue. Observability in signals is a concept rooted in signal processing and controls. A system is considered observable if the internal state of the system can be estimated using only the sensor information. In a prognostic application, sensor signals intended to detect wear will also contain some amount of noise. This case, noise is anything that is not the wear-out mode. It encompasses everything from random variations of the signal, to situations where the detection is intermittent or inconsistent. Hence, processing the raw sensor signal to maximize the wear-out precursors and minimize noise will provide an overall benefit to the detection before thresholds are applied. The proposed solution is a filter tuned to maximize detection of the wear-out mode. The evaluation of the filter is crucial, because that is also the evaluation of the entire prognostic. The problem statement transforms from a binary classifier to a discrete event detection using a continuous signal. Now, we can incorporate the time dimension and require a minimum lead time between a prognostic alert and the event. Filter evaluation is fundamentally performance evaluation for the prognostic detection. First, we aggregate the filtered values in a prescribed lead interval n samples before each event. Each lead trace is averaged so that there is one characteristic averaged behavior before an event. In this characteristic trace, we can consider the value at some critical actionable time, tac, before the event, after which there is insufficient time to act on the alert. The filtered signal value at this critical time should be anomalous, i.e. it should be far from its mean value. Further, the filtered value in the interval preceding tac should transition from near-average to anomalous. Both the signal value at tac­ as well as the filtered signal behavior up to that point present independent evaluation metrics. These frame the prognostic detection problem as it should be stated, as a continuous signal detecting a discrete event, rather than a binary classifier. A strong anomaly in the signal that precedes events on an aggregated basis is the alternate performance metric. If only a subset of events show an anomaly, that means the detection failure mode is unique to those events, and the performance can be evaluated accordingly. Thresholding is the final step, once the detection is optimized. The threshold need not be ambiguous at this step. The aggregated trace will indicate clearly which threshold will provide the most value.


2021 ◽  
Vol 12 (2) ◽  
pp. 1051-1060
Author(s):  
Wonjun Seo ◽  
Seokyeon Im ◽  
Geesoo Lee

Abstract. As fossil fuel depletion and environmental pollution problems are becoming increasingly more serious, interest in the efficient use of natural resources and alternative energy is rapidly growing. In particular, interest in fuels stored as high-pressure gases such as natural gas and hydrogen is also rising. Ultrasonic waves show various received signals according to characteristics such as density of the medium and acoustic impedance. An experimental study on the detection of the micro-leakage of fuel stored as high-pressure gas was conducted based on the characteristics of ultrasonic waves. First, an ultrasonic sensor was manufactured by selecting the matching layer with consideration of the acoustic impedance. In the experiment, a mass flow controller (MFC) was attached to a perforated hole in the fabricated chamber to generate micro-leakage, and the signal from the receiving ultrasonic sensor was then collected. The envelope signal of the received ultrasonic sensor signal was analyzed through the Gaussian distribution method. The temperature inside the chamber and the received voltage decreased according to a similar trend and showed a nonlinear result. However, the phase of the received ultrasonic sensor signal showed a relatively linear result according to the internal pressure change. Micro-leakage could not be detected with only the received voltage seen by the ultrasonic sensor. Therefore, the phase shift of the receiving ultrasonic sensor can be used to detect micro-leakage in a high-pressure gas tank.


Author(s):  
Haesung Jung ◽  
Jaesung Lee ◽  
Jinuk Park
Keyword(s):  

2021 ◽  
pp. 433-442
Author(s):  
Hanna Ukhina ◽  
Valerii Sytnikov ◽  
Oleg Streltsov ◽  
Pavel Stupen ◽  
Volodymyr Kudria

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xinjian Ma ◽  
Shiqian Liu ◽  
Huihui Cheng ◽  
Weizhi Lyu

Purpose This paper aims to focus on the sensor fault-tolerant control (FTC) for civil aircraft under exterior disturbance. Design/methodology/approach First, a three-step cubature Kalman filter (TSCKF) is designed to detect and isolate the sensor fault and to reconstruct the sensor signal. Meanwhile, a nonlinear disturbance observer (NDO) is designed for disturbance estimation. The NDO and the TSCKF are combined together and an NDO-TSCKF is proposed to solve the problem of sensor faults and bounded disturbances simultaneously. Furthermore, an FTC scheme is designed based on the nonlinear dynamic inversion (NDI) and the NDO-TSCKF. Findings The method is verified by a Cessna 172 aircraft model under bias gyro fault and constant angular rate disturbance. The proposed NDO-TSCKF has the ability of signal reconstruction and disturbance estimation. The proposed FTC scheme is also able to solve the sensor fault and disturbance simultaneously. Research limitations/implications NDO-TSCKF is the novel algorithm used in sensor signal reconstruction for aircraft. Then, disturbance observer-based FTC can improve the flight control system performances when the system with faults. Practical implications The NDO-TSCKF-based FTC scheme can be used to solve the sensor fault and exterior disturbance in flight control. For example, the bias gyro fault with constant angular rate disturbance of a civil aircraft is studied. Social implications Signal reconstruction for critical sensor faults and disturbance observer-based FTC for civil aircraft are useful in modern civil aircraft design and development. Originality/value This is the research paper studies on the signal reconstruction and FTC scheme for civil aircraft. The proposed NDO-TSCKF is better than the current reconstruction filter because the failed sensor signal can be reconstructed under disturbances. This control scheme has a better fault-tolerant capability for sensor faults and bounded disturbances than using regular NDI control.


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