Synchronous Averaging Research Articles

Fault diagnosis of a planetary gearbox by D norm-based time synchronous averaging (DTSA) with roughly estimated phase information under an encoder-less operating condition

Minimum entropy deconvolution (MED) filter is widely used to extract fault-related impulse features from noisy vibration signals for fault diagnosis of a gearbox. However, the MED process is vulnerable to abrupt environmental and operational noise, as disturbance components can be extracted instead of fault-related features. To mitigate this issue, time-synchronous averaging (TSA) can be employed to reduce the remaining noise of the MED-filtered signal further. However, TSA requires accurately resampled vibration signals with the aid of an additional encoder system, which could be unavailable in the actual field. Although a typical phase estimation method can be employed, the phase estimation accuracy cannot be precise enough for TSA. In addition, TSA cannot fundamentally recover the fault-related features if the conventional MED filter is undesirably optimized to extract a single dominant impulse feature while eliminating fault-related features. To overcome these challenges, this paper proposes the novel use of D norms for TSA (DTSA). It was found in this paper that D norms, which encapsulate results from multiple MED filters, are amplified at every fault position with margins equal to the length of the MED filter, enabling Dnorm-based TSA (DTSA) by alleviating the slight phase error from vibration signals under an encoder-less condition. In addition, D norms contain all of the fault-related features from the measured signal and thus prevent undesirable eliminations of fault-related features as opposed to the conventional MED process. It was found in this paper that case studies involving an analytic simulation and a 2-kW planetary gearbox testbed indicate that the proposed method outperforms the conventional MED process under a slightly varying speed condition even without an encoder.

Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines

The theoretical and experimental evaluation of the cyclostationary random data transferring process corrupted by the individually and jointly cyclostationary crosstalk interference is presented. The interference and the message signals were measured by the real time digital oscilloscope. Autocorrelation functions were evaluated by synchronous cyclic averaging procedure. The analyzed periodic two-dimensional impulse response of the time-varying filter allows to obtain the output random process with the same cyclic frequency at the output of the filter by separation of orthogonal stationary waveforms constituting the input cyclostationary random process (CSRP). The filtering of the measured random process was implemented by the cyclic Wiener filter. The evaluation of the two-dimensional autocorrelation function and eye diagrams at the output of the cyclic Wiener filter showed significant reduction of the independent interference components in the estimated message signal.

Development of crack induced impulse-based condition indicators for early tooth crack severity assessment

Diagnosis of gearbox tooth cracks at an early stage is important to prevent catastrophic failures. The time synchronous average (TSA) of vibration signals of gearboxes with a tooth crack mainly consists of the gear meshing frequency (GMF) and its harmonics, the crack-related amplitude modulation and frequency modulation (AM-FM) and the crack induced impulse (CII). To conduct tooth crack diagnosis, many diagnostic techniques have been developed based on the TSA signal. However, most of them achieved poor performance in terms of assessing tooth crack severity in the early crack stage. The main problem is that they are not developed using the signal components that can well indicate early tooth crack severity progression. To overcome this problem, this paper proposes a method to conduct a thorough analysis on the CII since it provides more valuable information on tooth cracks than other components. In the proposed method, the GMF and its harmonics and the crack-related AM-FM are first removed from the TSA signal using comb notch filters. The notch filtered signal mainly contains components related to the CII. A modal model is adopted to fit the dominant resonances in the notch filtered signal and modal parameters are obtained using the matrix pencil method. With the modal parameter estimates, the dominant resonances, which are single degree-of-freedom impulse responses (SDOF IRs), are identified and used to reconstruct the CII. Besides, the SDOF IRs are grouped according to their carrier frequencies. The energy of the reconstructed CII and the sum of the energy of the SDOF IRs with carrier frequencies in a specific frequency band are proposed as two new condition indicators (CIs) for tooth crack diagnosis. The effectiveness of the proposed method and the new CIs for early tooth crack severity assessment are demonstrated using simulated gearbox vibration signals and experimental gearbox vibration datasets.

Vibrational Monitoring of Nested Planetary Geartrain with Unground Pinion

The nested planetary gear train, which has two integrated single-stage planetary gearsets, is one of the newly developed compound gear train that has been successfully applied to the automobile transmissions. In the current study, a certain type of gear fault in the nested gear train, ungrounded pinion, is investigated using a non-destructive approach monitoring its vibration levels. A novel experimental test stand with open and vertical setup has been designed to collect the vibrational data by mounting the accelerometer directly to the gear clutches. Each of the two layers of the compound gear was tested separately. The measured vibrational data were processed with several signal processing techniques, which includes (a) frequency spectrum analysis, (b) time synchronous averaging (TSA) and (c) modulation sideband analysis. The experimental results show that the existence of the ungrounded pinion can be identified with the frequency spectrum analysis of the vibrational data. In addition, the modulation sidebands are also modeled using a modified version of the traditional technique of physical signal modeling. It is shown that the relative phase of the planet and the meshing vibration strength changed by the unground gear is the critical factor for determining the modulation sideband behavior. In addition, the location of the ungrounded pinion can also be determined by the time history processed by TSA.

Improved time domain synchronous averaging based on the moving interpolation and kurtosis criterion searching

The fault diagnosis of rotating machinery involves revealing the possible faults in advance to reduce dispensable breakdowns; the difficulty of this lies in identifying the periodic features of rotating machines contaminated by background noise. Time-domain synchronous averaging (TSA) has been studied to eliminate random noise in signals. However, TSA sometimes cannot extract useful signals more accurately because of the accumulative phase error caused by the discrete sampling process and the difficulties in obtaining accurate prior information. Hence, the moving interpolation and Kurtosis searching criterion are used for more accurate extraction of harmonics and transient impacts. Also, an improved compensation algorithm based on moving interpolation is proposed to overcome the amplitude attenuation caused by cumulative phase error for low signal to noise ratio (SNR) signal. To determine some parameters in the algorithm such as the number of periods and the time delay of windows which depend on a priori information relevant to the fault period, a searching method for the prior information in vibration signals including transient impacts and harmonics with Kurtosis and minimum mean square error criterion is proposed to optimize the algorithm in the process of feature extraction. Finally, the improved TSA (ITSA) is applied to extracting the fault features in a real factory, and the performance of fault feature extraction in low SNR signal conditions with the ITSA has been enhanced effectively.

Application of vibro-acoustic methods in failure diagnostics

The present study is concerned with the presentation of diagnostic strategies and methods based on vibro-acoustic signals that are able to check the operation of mechanical structures (e.g. internal combustion engines) or to detect faults on test bench during operation. The main goal of this study is to summarize and review the state-of-the art monitoring procedures and diagnostic methods based on vibrational and acoustical signals by the help of literature sources. A further goal is to familiarize the reader with the theoretical background of these procedures and concepts adjusted to the topic. The paper seeks to illustrate the application of each methods through practical examples, so it may be useful for those who wish to understand what kind of fault can be detected by using vibro-acoustic diagnostic systems. Last, but not least, the arose difficulties and limitations associated with use of this methods are mentioned. This paper is focusing principally on some of signal - based methods in time - and frequency domain, such as Fast Fourier Transformation, Short Time Fourier Transformation, orbit portraits, symmetrized dot pattern method and time synchronous average.

A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging

Background Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality. Objective The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability. Methods During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes. Results The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal. Conclusion This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.

Rapid detection of cracks in the rail foot by ultrasonic B-scan imaging using a shear horizontal guided wave electromagnetic acoustic transducer

Transverse cracks in the rail foot are responsible for the breaking of the rail. The existing non-destructive testing technology for high-speed railway track is difficult to perform in-situ and rapid detection for the rail foot, which poses a hidden danger to driving safety. In this study, a new method is proposed for the in-situ and rapid detection of cracks in the rail foot by ultrasonic B-scan imaging. A finite element method (FEM) based on the Bloch–Floquet boundary and domain constraint were employed to calculate the dispersion curves of shear horizontal (SH)-like guided waves propagating in the rail foot. SH guided waves mainly vibrate with in-plane displacement, and they are less affected by the rail components such as elastic clips and pads. Based on the calculated dispersion curves, an SH-guided wave electromagnetic acoustic transducer (EMAT) with a center frequency of 0.154 MHz was developed, and ultrasonic B-scan imaging was performed to detect straight cracks in the rail foot. Besides, the synchrosqueezed wavelet transform (SWT) method was proposed to remove noises and higher-order guided wave modes from the original ultrasonically detected signals. Results show that FEM with the Bloch–Floquet boundary and domain constraint can be used to solve the dispersion curves of the SH-like guided waves of the rail foot accurately and easily. The application of SWT can increase the lift-off of the EMAT, eliminate the noise, remove the higher-order SH-like guided wave modes, improve the detection efficiency, and enhance the quality of ultrasonic B-scan images. After the SWT processing, the signal-to-noise ratio (SNR) of the ultrasonic signal is increased by at least 5.98 dB. When the lift-off of the EMAT is 4 mm and without synchronous averaging, the on-line and rapid ultrasonic B-scan imaging of the rail foot using guided wave EMAT can still achieve a reasonable SNR.

Modal parameter identification of rotating machinery based on time domain synchronous averaging

Operational modal analysis (OMA) is an important method of structural dynamic design and mechanical fault diagnosis. It is the application of parameter identification in the field of engineering vibration. Traditional operational modal analysis is based on the random response of white noise. However, when rotating machinery structure is running at high speed, it will produce a lot of harmonic interference, which will affect the identification effect. In this paper, the time domain synchronous averaging technique is used to remove the periodic harmonics in the response. For the random response part, the method of combining NExT and ITD is used to identify the modal parameters.

Research on Wind Turbines Fault Diagnosis Technology Based on CMS Data Feature Extraction

As a rich clean and environmentally friendly renewable resources, wind energy has emerged as a strategic choice for countries around the world. Because the wind turbines often operate in severe working conditions such as variable load and large temperature difference they are prone to failures and possible shutdowns. The shutdowns however seriously affect the economic benefits of the wind turbines. Initiative maintenance has become a worldwide recognized scientific method for planning and determining preventive maintenance work, the implementation of this strategy relies on real-time condition monitoring and fault signal identification methods. The condition monitoring of wind turbine can help master the health state and power generation performance of wind turbine, so as to timely formulate maintenance strategies and adopt technical modification measures to improve power generation performance, reduce the down time of wind turbine, avoid the occurrence of major faults, save maintenance cost and improve power generation capacity. Therefore, a condition monitoring system is built on a wind turbine of Zhangjiakou, and a systematic signal analysis method is proposed, time-domain synchronous averaging technology, based on variable period, impulse signal feature extraction technology based on Teager and signal decomposition technology based on CEEMD. The proposed method realizes the signal analysis and feature extraction of non-stationary nonlinear, weak signal and frequency aliasing signals, and successfully diagnose the gearbox secondary meshing failure during the long-term monitoring. This confirms that the monitoring system methods and signal analysis technology proposed in this paper can effectively realize the condition monitoring and fault diagnosis of wind turbines.

Methods for enhancement of high-sensitivity detection for a surface wave EMAT

Due to the poor conversion efficiency and signal-to-noise ratio (SNR) of Electromagnetic Acoustic Transducer (EMAT) testing, the defect detection sensitivity is limited, which restricts the extensive industrial applications. A finite element model for the testing process of a meander-coil EMAT was established that considers the simplified excitation and detection circuits for the EMAT. Based on this model, the effect of the connection methods (parallel or series) of the coils in the generating and receiving EMATs on their generating and receiving efficiency was investigated, and the simulation results were validated experimentally. Subsequently, the pulse compression technique with a 13-bit Barker code was used for the EMAT detection, and improvements of the SNR and range resolution were established through numerical simulation and experimental measurement. The results show that compared with the traditional EMAT design, which comprises single-layer transmitting and receiving coils, the conversion efficiency of the optimized EMAT with two excitation and receiving coils in the parallel connection can be improved by 52.8%. With the application of the Barker-coded pulse compression to detect the ultrasonic A-scan signal with no synchronous average, the SNR of the defect echo can be improved by 9.5 dB compared with the A-scan signal with 128 synchronous averages.

An on-board detection framework for polygon wear of railway wheel based on vibration acceleration of axle-box

The polygon wear of railway wheel (PWRW) is a wear fault that is ubiquitous in railway vehicles. PWRW can induce a strong periodic excitation to both vehicle and track, which not only decreases passenger comfort but also is detrimental to the operational reliability and safety. Both the degree and the order of PWRW are important parameters used to quantify the fault. Because the fault-related components distribute at a wide range in the frequency domain, it is easy to alias with some radiated vibrations from vehicle and track components, which makes the on-board detection for both parameters of PWRW very difficult. To address the practical engineering problem, this paper proposes a detection framework based on the angle domain synchronous averaging technique (ADSAT). The detection method employs the vertical axle-box vibration acceleration (ABVA), which is easy to obtain and can also be used to monitor the conditions of axle-box bearings. The paper compares the proposed and traditional methods. The results reveal that the proposed method not only achieves the order detection which the traditional method cannot, but also mitigates the influence of background noise. The feasibility and effectiveness of the proposed method to improve the detection accuracy of PWRW is demonstrated through simulation and real field investigations.

A New Acoustic Emission Sensor Based Gear Fault Detection Approach

In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore, developing effective gearbox fault detection tools is important in the PHM of wind turbine. PHM system allows less costly maintenance because it can inform operators of needed repairs before a fault causes collateral damage happens to the gearbox. In this paper, a new acoustic emission (AE) sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA) and spectral kurtosis (SK) toprocess AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne techniques commonly used in communication are used to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from MHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

Envelope Analysis on Vibration Signals for Stator Winding Fault Early Detection in 3-Phase Induction Motors

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor condition-based maintenance methods usually involve current and voltage measurements, which could be expensive to collect, and vibration-based analysis is often only capable of detecting the fault when it is already quite significant. In contrast, the solution presented in this study provides a refreshing perspective by applying time synchronous averaging to remove the discrete frequency component, and amplitude demodulation to further enhance the signal with the help of kurtogram. Experimental results on a three-phase induction motor show that the method is also able to distinguish different fault severity levels.

Amplitude and phase interaction in Hilbert demodulation of vibration signals: Natural gear wear modeling and time tracking for condition monitoring

In the context of automatic and preventive condition monitoring of rotating machines, this paper revisits the demodulation process essential for detecting and localizing cracks in gears and bearings. The objective of the paper is to evaluate the performance of the well-known Hilbert demodulation by providing a quantified assessment in terms of signal processing. For this purpose, vibration test signals are simulated guided by the analysis of real-world measurements. The database comes from a natural wear experimentation on a test bench at an industrial scale and without any fault initiation. In the proposed simulated model, the amplitude modulation is designed in a physical approach in order to be able to set up the number of faulty teeth and their location. The impact of a limited spectral bandwidth filtering is quantified not only for the amplitude but also for the phase modulation estimations. The interactions between the amplitude and phase estimations are discussed. A focus is made on the analytic signal ambiguity due to the non-uniqueness of the amplitude estimation. This property induces an original investigation when demodulating the residual generated after a time synchronous averaging. Finally, as the objective is a continuous surveillance of a machine, results are given for a sequence of real-world measurements in order to visualize the fault evolution through the demodulation process.

Diagnostics of Local Tooth Damage in Gears by the Wavelet Technology

The chipped gear tooth appearance is normally a result of the initial fatigue damage in a tooth. It is a special gear failure mode and differs from local fatigue damage of gear teeth. Therefore, diagnosis of chipped gear tooth requires a special investigation. Recently, the novel gear damage diagnosis technology, based on the wavelet transform was proposed and successfully applied for diagnosis of the early stage fatigue damage. The proposed technology is applied in this study for diagnostics of a partly-missing (chipped) tooth in a gear of the Machine Fault Simulator (MFS). The advanced automatic technology for the time synchronous averaging of the raw gear vibrations has been employed in this study; this technology does not require speed data. An advanced decision making technique based on use of the likelihood ratio allowed for the continuous correct diagnosis of chipped teeth throughout the recorded data without false alarms and missed detections. The likelihood ratio was obtained using the Gaussian models of the data for classes “undamaged” and “damaged”.

Vibration analysis of planet gear bore-rim failure using enhanced planet time synchronous averaging

It is a worldwide challenge to detect planet gear bore-rim cracking using vibration data analytics, especially for large helicopters like the Airbus Super Puma that has experienced two fatal accidents since 2009 due to planet gear bore-rim cracking. For helicopter safety, it is of vital importance to equip helicopter vibration health monitoring systems with the capability of detecting and monitoring the progression of planet gear bore-rim cracking. When a crack reaches sufficient size in the rim of the gear, the weakening stiffness caused by the crack allows the planet gear teeth near the crack to distort as they come into and out of mesh, resulting in vibration impacts. A key challenge for detecting a planet gear bore-rim crack lies in isolating these impacts from the dominating gear mesh harmonics. In this paper, we propose a method of vibration data analytics based on enhanced planet gear time synchronous averaging (EP-TSA). The method takes advantage of visualizing the impacts from planet-ring and planet-sun gear meshes over the planet-ring hunting tooth period of the planetary gear system. We firstly process the raw vibration signal using time-synchronous averaging with respect to the relative rotation of the planet gear over a number of hunting tooth periods. The method employs a novel resampling scheme, termed boosted synchronous resampling scheme (BSRS), which facilitates simultaneous removal of multiple shaft and gear mesh harmonics and their intermodulation components using the spectrum of the averaged signal in order to derive a residual signal. By construction, the BSRS facilitates three-dimensional (3-D) plotting of the squared envelope of the residual signal, comprising multiple individual revolutions of the planet gear over the hunting tooth period. With crack-induced impacts visualized in 3-D, we can identify those planet gear revolutions where the crack-induced impacts are in close proximity to the vibration transducer to obtain a final feature signal reflecting the characteristics of the bore-rim cracking. We call the final feature signal the enhanced planet residual signals (EPRS), which is those individual residual signals at the selected planet gear revolutions with the largest kurtoses above a certain threshold. Where possible we should look at multiple planet gear revolutions to reinforce the detection of vibration impacts. Lastly, we confirm the detection of planet gear bore-rim cracking by the consistent pattern of two spikes in each planet revolution that are associated with the planet-ring and planet-sun gear meshes. The method is validated using vibration data generated from a small industrial planetary gearbox with simulated cracks inserted in the bore-rim from one side of a planet gear. The results show that the BSRS can be very effective in removing multiple sets of shaft harmonics and their intermodulation sidebands. We have demonstrated that the visualization tool can provide strong evidence of the crack-induced impacts. In comparison to other methods our results have shown that we can detect the planet gear bore-rim crack more effectively using the enhanced planet time synchronous averaging method.

A carrier wave extraction method for cavitation characterization based on time synchronous average and time-frequency analysis

Cavitation detection is important in ensuring the reliability of fluid machinery, such as pumps. Vibration signal analysis is widely accepted as an effective tool in condition monitoring and fault diagnosis due to its ability to obtain substantial information and convenience of sensor arrangement. However, cavitation characterization based on vibration measurement is challenging because of the complicated underlying mechanism and low signal-to-noise ratio (SNR) of actual data. This study proposes a carrier wave extraction method for cavitation characterization by combining time synchronous average and time-frequency analysis (TATF) based on amplitude-modulated (AM) signal theory. The proposed method can reasonably measure cavitation severity by distinguishing time-frequency characteristics between different cavitation states. Compared with traditional vibration/acoustic signal monitoring or intelligent diagnostic techniques, cavitation detection based on TATF has the advantages of accurate classification and outstanding physical significance. First, cavitation state division criterion based on energy indicator is proposed. Its superiority is verified via comparison with the traditional criterion of hydraulic head. Second, the vibration signal model of pumps is established as an AM signal model, and the modulation mechanism is elaborated. Extraction of carrier wave components caused by cavitation is regarded as the critical issue in cavitation characterization. Then, TATF is described detailedly and its effectiveness is validated by simulation signals and actual data. Finally, the intelligent classification results of cavitation state by deep convolutional neural network (DCNN) demonstrate the superiority of TATF over short-time Fourier transform (STFT) and wavelet transform (WT).

Torque measurements from MW wind turbine Gearboxes: a system identification approach

The gearbox is a critical component of modern MW wind turbines. An accurate model of the gearbox dynamics is needed to improve gearbox design, develop advanced control algorithms, and more effective fault diagnosis tools which could lead to lower the cost of energy from wind. The objective of this paper is to investigate how torque measurements can be used in a data-driven framework to build dynamic models of wind turbine gearboxes.An initial torsional model has been derived from first principles considering the stiffness of the gears, shafts, and structural components in the gearbox together with the mechanical components of the test bench. This model has been used to create simulated data of the experiments performed on gearboxes and to apply system identification techniques to the simulated signals, with a focus on predictor based subspace identification methods. System identification has been applied to torque and speed data measured on physical tests of two 3.4MW gearboxes. Gearbox excitation frequencies and their harmonics dominate the measured signals and disturb the system identification algorithms. Several techniques have been investigated to remove the shaft rotation and gear mesh frequency harmonics of the torque and rotational speed signals based on time synchronous averaging.

Simultaneous Measurements of Capacitive Electrocardiogram and Ballistocardiogram Using In-Bed Fabric Sheet Electrode for Blood Pressure Estimation.

We recently reported the possibility of the simultaneous measurement of capacitive electrocardiogram (cEGC) and capacitive ballistocardiogram (cBCG) using non-contact capacitive coupling systems in bed. However, the relations of the two measurements with blood pressure (BP) have not been elucidated and cBCG measurements were unstable. Therefore, in this study, we improved the electrode and circuit, and succeeded in stable simultaneous measurement of cECG and cBCG. Synchronous averaging was performed to confirm the similarity of the waveform to the reproducibility of cBCG. Valsalva test (VT) was performed on seven subjects, and four verification intervals were set after VT. Then, the estimated blood pressure value was calculated using the instantaneous heart rate as the time difference parameter. We examined the relationship between estimated systolic blood pressure (eSBP) and systolic blood pressure (SBP) and observed a high positive correlation. cBCG contains components that are strongly related to BP. In the future, the electrode will be improved to measure cBCG at two points simultaneously at regular intervals to avoid shifting the Pulse Beat Arrival Time (PBAT) time axis. Further, in order to enhance the accuracy of blood pressure estimation, a change in the respiratory phase is incorporated into the estimation of the blood pressure value.