E-Textile Metamaterials: Stop Band Pass Filter

In this paper, the utilization of common fabrics for the manufacturing of e-textile metamaterial is investigated. The proposed design is based on a transmission line loaded with split-ring resonators (SRRs) on a cotton substrate for filter signal application. The proposed design provides a stop band between 2.7 GHz and 4.7 GHz, considering a four stage SRR topology. Experimental results showed stop band levels higher than −30 dB for the proposed compact embroidered metamaterial e-textiles. The validated results confirmed embroidery as a useful technique to obtain customized electromagnetic filter properties, such as transmitted signal filtering and control, on wearable tech device applications.

Title Fault Diagnosis Method of Gearbox Multifeature Fusion Based on Quadratic Filter and QPSO-KELM

Effective filtering and noise reduction, feature extraction and fault diagnosis, and prognostics technology are important to Prognostics and Health Management (PHM) of equipment. Therefore, a multifeature fusion fault diagnosis method based on the combination of quadratic filtering and QPSO-KELM algorithm is proposed. In the quadratic filtering, stable filtering can reduce the impact of noise and fast-kurtogram can filtrate fault frequency bands with rich fault information. Then, the time-domain, frequency-domain, and time-frequency parameters of the secondary filter signal are extracted. MSSST was used to analyze the filtered signal, and the time-frequency image was obtained. The time-frequency parameter was extracted from the time-frequency image by 2DPCA, and all the extracted parameters are taken as the fusion fault feature of the gearbox. Finally, the fault feature parameters are taken as the training sample and testing sample of QPSO-KELM for training and testing to achieve the purpose of fault diagnosis. The experimental results show that the proposed method can effectively filter the noise, complete the fault mode identification of gearbox, and improve the fault diagnosis accuracy better than other methods.

Design of a New Long-Time Continuous Photoplethysmography Signal Acquisition System to Obtain Accurate Measurement of Heart Rate

This study presents a time-interleave and low DC drift long-time continuous photoplethysmography (PPG) signal acquisition system to obtain accurate measurement of heart rate (HR) in real-time. Time-interleave functionality is used herein to minimize the mispositioning issue. Intensity tuning and transimpedance amplifier gain tuning is used herein to acquire a high-quality PPG signal. The front-end analog readout circuit is designed and implemented by using T18 process. The experimental result shows that the design readout system has the DC settling time of 1 second after the motion artifact. The measured current sensing range is 30nA–10uA. The estimated signal to noise ratio is 68dB@1Hz. The backend pre-signal processing incorporates a new convolution-based moving average filter, signal quality index estimator, and a peak-through detector. The non-invasive PPG sensor is applied to the wrist artery of the 40 healthy subjects for sensing the pulsation of the blood vessel. During the measurement, the subject did not drink (alcohol), eat, smoke or workout. The Measurement results shows that the heart rate accuracy and standard error are 95%, and 0.37±1.96bpm, respectively.

Kalman Filter for Noise Reducer on Sensor Readings

Most systems nowadays require high-sensitivity sensors to increase its system performances. However, high-sensitivity sensors, i.e. accelerometer and gyro, are very vulnerable to noise when reading data from environment. Noise on data-readings can be fatal since the real measured-data contribute to the performance of a controller, or the augmented system in general. The paper will discuss about designing the required equation and the parameter of modified Standard Kalman Filter for filtering or reducing the noise, disturbance and extremely varying of sensor data. The Kalman Filter equation will be theoretically analyzed and designed based on its component of equation. Also, some values of measurement and variance constants will be simulated in MATLAB and then the filtered result will be analyzed to obtain the best suitable parameter value. Then, the design will be implemented in real-time on Arduino to reduce the noise of IMU (Inertial Measurements Unit) sensor reading. Based on the simulation and real-time implementation result, the proposed Kalman filter equation is able to filter signal with noises especially if there is any extreme variation of data without any information available of noise frequency that may happen to sensor- reading. The recommended ratio of constants in Kalman Filter is 100 with measurement constant should be greater than process variance constant.

Nodal screw compressor failure analysis using data analytics—CSG application

An engine-driven oil-injected screw compressor in CSG service failed catastrophically. Instrumentation provided on the package was ineffective in predicting or detecting the failure. As part of the Root Cause Analysis (RCA) process, a statistical analysis of the logged instrument data, as measured across a period of six months prior to the failure, was carried out. This paper uses data analytic methods to process instrument data, data visualisation techniques, advanced statistical analysis of the instrument data, and techniques to filter signal noise. The analysis recognised the multivariate behaviour and interrelationships between various operating parameters. The paper further provides insight into the interpretation of statistical measures and how to draw conclusions that explain the failure mechanism. The outcomes of the analysis presented in this paper then provided insights into establishing operating envelopes, proposed instrumentation upgrades to be provided in future and helped establish an operation and maintenance regime that should assist in preventing such failures in future.