Multi-distance Measurement Research Articles

Scalable multi-distance measurement approach for the optical assessment of tooth-individual shape parameters of large gearings

Abstract The demand for scalable measuring systems for the fast measurement of tooth-individual shape parameters of large gears is increasing. Due to a limited measuring speed and a limited measuring volume, standard tactile gear measuring systems are only conditionally suitable for comprehensive measurements of large gears. Therefore, the applicability of a scalable optical multi-distance measurement approach consisting of an optical sensor and a rotary table with a model-based evaluation is studied. As a fundamental gear shape parameter, the base circle radius of a large gearing is evaluated and the achievable measurement uncertainty is estimated. As a result, the tooth-individual base circle radius is determined on average with an expanded measurement uncertainty (k = 2) of one quarter of the required tolerance, and the realized measurement setup enables the measurement of all teeth within 1 minute. Hence, the scalable optical multi-distance measurement approach is appliacable for the fast measurement of tooth-individual shape parameters of large gearings.

A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex.

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

Determination of the mean base circle radius of gears by optical multi-distance measurements

Abstract. The required reliability of wind turbine gearboxes increases the requirements for large gear measurements. Extensive measurements to reliably assess the geometry of large gears in the single micrometer range are necessary. Due to an individually fixed measuring volume, standard methods like coordinate and gear measuring instruments reach their limits for large gears with diameters > 1 m. Therefore, a scalable optical measurement approach consisting of a single sensor in combination with a rotary table for multi-distance measurements with subsequent model-based evaluation of shape parameters of gears is presented. The scalable measurement approach is to be extended to a multisensory system in further work. As a fundamental shape parameter the mean base circle radius using the example of spur gears is determined. The base circle radius is used due to the geometric relationship to further shape parameters for example to the profile slope deviation. The theoretically achievable measurement uncertainty of the mean base circle radius due to sensor noise is estimated to less than 5 µm (k=2) for a small and a large gear, which verifies the scalability of the sensor system. In order to show a general proof of principle, two series of optical measurements on a gear with a diameter of 0.105 m are performed and referenced with a tactile measurement. As a result, random errors of 1.2 µm for k=2 are determined. The remaining systematic deviations to the reference value amount to 4.3 and 1.6 µm, respectively. Hence, the total measurement uncertainty is currently limited by systematic effects, and the defined aim of a total uncertainty of less than 5 µm (k=2) is narrowly missed by 1.5 µm. The random errors of 1.2 µm (k=2) show, however, that an adequate measurement precision is achieved and that the multi-distance measurement approach has the potential to reach the aimed measurement uncertainty with appropriate strategies to compensate for the systematic influences. The experimental and theoretical results prove the principle applicability of the proposed single sensor multi-distance approach for the precise inspection of gears.

Gear Shape Parameter Measurement Using a Model-Based Scanning Multi-Distance Measurement Approach.

To reduce wind turbine failures by defective drive trains, deviations in the geometry of large gears (diameter ≳ 1 m) must be extensively determined with single-digit micrometer uncertainties. Fixed measuring volumes limit standard measuring methods like coordinate and gear measuring instruments for large gear measurements. Therefore, a model-based scanning multi-distance measurement approach for gear shape parameters is presented. The measurement approach has a scalable design and consists of a confocal-chromatic sensor, rotary table as a scanning unit and model-based signal processing. A preliminary study on a midsize spur gear demonstrates the general feasibility of the model-based scanning multi-distance measurement approach. As a result, the mean base circle radius as the fundamental gear shape parameter is determined with an uncertainty of <5 μm. The calibration and adjustment of the sensor arrangement were performed with a known calibration gear. Scalability is not experimentally validated in this article. However, simulations verify the scalability of the measurement approach in a first step. For gears with 1 m in diameter and varying tooth flank geometries, the estimated achievable uncertainty of the mean base circle radius is still <5 μm. Therefore, the model-based scanning multi-distance measurement approach is a promising alternative for gear inspection.

Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments.

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

EEMD and bidimensional RLS to suppress physiological interference for heterogeneous distribution in fNIRS study

Near-infrared spectroscopy (NIRS) can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical, which can be deployed for the cerebral function study. However, NIRS-based cerebral function detection accuracy can be significantly influenced by the physiological activities such as cardic cycle, respiration, spontaneous low-frequency oscillation and ultra-low frequency oscillation. The distribution difference of the capillary, artery and vein leads to the heterogeneity feature of the cerebral tissues. In the case that the heterogeneity is not serious, good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach. The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious. In this study, the cerebral function activity signal is extracted using recursive least square (RLS) method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition (EEMD) algorithm. The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal. Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology. The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.

Evoked Hemodynamic Response Estimation to Auditory Stimulus Using Recursive Least Squares Adaptive Filtering with Multidistance Measurement of Near-Infrared Spectroscopy.

The performance of functional near-infrared spectroscopy (fNIRS) is sometimes degraded by the interference caused by the physical or the systemic physiological activities. Several interferences presented during fNIRS recordings are mainly induced by cardiac pulse, breathing, and spontaneous physiological low-frequency oscillations. In previous work, we introduced a multidistance measurement to reduce physiological interference based on recursive least squares (RLS) adaptive filtering. Monte Carlo simulations have been implemented to evaluate the performance of RLS adaptive filtering. However, its suitability and performance on human data still remain to be evaluated. Here, we address the issue of how to detect evoked hemodynamic response to auditory stimulus using RLS adaptive filtering method. A multidistance probe based on continuous wave fNIRS is devised to achieve the fNIRS measurement and further study the brain functional activation. This study verifies our previous findings that RLS adaptive filtering is an effective method to suppress global interference and also provides a practical way for real-time detecting brain activity based on multidistance measurement.

Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy—how robust are the different calculation methods against movement artifacts?

Continuous-wave near-infrared spectroscopy and imaging enable tissue hemodynamics and oxygenation to be determined non-invasively. Movements of the investigated subject can cause movement artifacts (MAs) in the recorded signals. The strength and type of MAs induced depend on the measurement principle. The aim of the present study was to investigate the quantitative relationship between different single-distance (SD) and multi-distance (MD) measurement methods and their susceptibility to MAs. We found that each method induces MAs to a different degree, and that MD methods are more robust against MAs than SD methods.

A CMOS Sensor for Measurement of Cerebral Optical Coefficients Using Non-Invasive Frequency Domain Near Infrared Spectroscopy

A heterodyned architecture is integrated with a 180 nm CMOS chip for use in portable frequency domain near infrared spectroscopy (fdNIRS) tools for real time monitoring of tissue oxygenation in the brain. The design and performance measurement results of this chip are summarized in this paper to demonstrate its applicability in multi-distance fdNIRS to measure the absorption and scattering coefficients of tissue. The 2.25 mm2 chip is integrated with four sensor channels, which have a high frequency low noise front end and information processing circuitry to interface with an avalanche photodiode to detect the high speed weak light signal. The four-channel sensor draws 40 mA of current from a 1.8 V power supply and uses an off-chip counter implemented on an FPGA to quantify the amplitude and phase information required for tissue characterization with and linearity error, respectively. An experiment using the multi-distance measurement is used to measure the optical properties of a homogeneous tissue phantom using the CMOS integrated instrument.

Monte Carlo study of global interference cancellation by multidistance measurement of near-infrared spectroscopy

The performance of near-infrared spectroscopy is sometimes degraded by the systemic physiological interference in the extracerebral layer. There is some systemic interference, which is highly correlated with the functional response evoked by a task execution. This kind of interference is difficult to remove by using ordinary techniques. A multidistance measurement method is one of the possible solutions for this problem. The multidistance measurement method requires estimation parameters derived from partial pathlength values of tissue layers to calculate an absorption coefficient change from a temporal absorbance change. Because partial path lengths are difficult to obtain, experimentally, we estimated them by a Monte Carlo simulation based on a five-layered slab model of a human adult head. Model parameters such as thickness and the transport scattering coefficient of each layer depend on a subject and a measurement position; thus, we assumed that these parameters obey normal distributions around standard parameter values. We determined the estimation parameters that provide a good separation performance in average for the model parameter distribution. The obtained weighting is robust to model parameter deviation and provides smaller errors on average compared to the parameters, which are determined without considering parameter distribution.

Detection and quantification of insoluble particles by ultrasound spectroscopy

The Ultrafood system, a custom-built diagnostic ultrasound device, is used to accurately measure the concentration of particulate matter in a fluctuating high temperature liquid system. The two main problems, of thermal expansion and thermal variation in ultrasonic outputs were tackled by multi-distance measurement and low frequency spectroscopy, respectively. The resulting techniques have application at laboratory, scale for investigation of particulate suspensions and for online process monitoring.