Precision degradation prediction of inertial test turntable based on Hidden Markov Model and optimized particle filtering

In order to solve the problem that there is no effective evaluation method for the precision degradation state of inertial test turntable, a prediction model for the position precision degradation trend of test turntable was proposed based on the Hidden Markov Model (HMM) algorithm and Particle Filter (PF) algorithm. The initial parameter of the PF algorithm was optimized by the Particle Swarm Optimization (PSO) algorithm. The vibration signal was selected as the research data, which could be obtained from an velocity test of turntable precision degradation. Firstly, the original vibration signal was denoised by Ensemble Empirical Mode Decomposition and Principal Component Analysis (EEMD-PCA) algorithm, and the signal with fault characteristic was extracted for signal reconstruction; Secondly, a HMM model could be trained by using the statistical characteristic values as observation matrix, and the diagnosis of early position precision degradation and the health state indexes could be obtained. Finally, a prediction model of the test turntable precision degradation could be established by using PF algorithm, and the Remaining Useful Life (RUL) of the test turntable precision could be calculated. When the 50th group data were taken as the prediction starting point, the predicted remaining useful life was 21 years, and the actual measured result was 17 years, which are close to each other. Comparing the model calculation results and the test measurement results, it is shown that the model could effectively and accurately predict the change trend and remaining useful life of the test turntable precision.

A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.

A novel approach to predict the precision sustainability of ball screw under multidirectional load states

The accurate model of the load state for all balls under multidirectional load is very helpful for the design process of ball screws. The contact deformation model of the ball screw without consideration of the stress difference of all balls is inaccurate. In this paper, a novel contact load model of the ball screw is established by considering coupled axial, radial load to study the elastic deformation displacement and position accuracy. The deviation and variation of axial elastic deformation with the dimension errors of all balls are investigated to obtain the influence of load state on the precision sustainability of the ball screw. The position precision including travel deviation and variation by considering load distribution of all balls is studied under the different load conditions. In addition, a new working bench is designed to study the position precision of the ball screw. The experimental study is carried out to obtain the relationship between the position precision and the contact load state of all balls, which is a reference to compensate for the precision loss of the ball screw.

Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning

In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe. Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.

A Mature ROMANCE: A Matter of Quantity and How Two Can Be Better than One

<div><div><div><p>Capillary electrophoresis coupled to mass spectrometry (CE-MS) is increasingly gaining momentum as an analytical tool in metabolomics, thanks to its ability to ob- tain information about the most polar elements in biological samples. This has been helped by improvements in peak robustness by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step towards the use of CE- MS for untargeted metabolomics data, the authors previously developed and introduced the ROMANCE software, with the purpose of automating mobilogram generation for large untargeted datasets while offering a simple and self-contained user interface. In natural continuation ROMANCE has been upgraded to its v2 to read other types of data (targeted MS data and 2D UV-like electropherograms), offer more flexibility in the transformation parameters (including field ramping delays and the use of sec- ondary markers), more measurement conditions (depending on detection and ionization modes), and most importantly tackle the issue of quantitative CE-MS. To prepare the ground for such an upgrade, we present a review of the current theoretical framework with regards to peak reproducibility and quantification, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. We then present the new version of the software, and validate it experimentally. We contrast the multiple marker mobility transfor- mations with previous results, finding increased precision, and finally we showcase an application to actual untargeted metabolomics data.</p></div></div></div>

A Mature ROMANCE: A Matter of Quantity and How Two Can Be Better than One

<div><div><div><p>Capillary electrophoresis coupled to mass spectrometry (CE-MS) is increasingly gaining momentum as an analytical tool in metabolomics, thanks to its ability to ob- tain information about the most polar elements in biological samples. This has been helped by improvements in peak robustness by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step towards the use of CE- MS for untargeted metabolomics data, the authors previously developed and introduced the ROMANCE software, with the purpose of automating mobilogram generation for large untargeted datasets while offering a simple and self-contained user interface. In natural continuation ROMANCE has been upgraded to its v2 to read other types of data (targeted MS data and 2D UV-like electropherograms), offer more flexibility in the transformation parameters (including field ramping delays and the use of sec- ondary markers), more measurement conditions (depending on detection and ionization modes), and most importantly tackle the issue of quantitative CE-MS. To prepare the ground for such an upgrade, we present a review of the current theoretical framework with regards to peak reproducibility and quantification, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. We then present the new version of the software, and validate it experimentally. We contrast the multiple marker mobility transfor- mations with previous results, finding increased precision, and finally we showcase an application to actual untargeted metabolomics data.</p></div></div></div>

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision.

NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm

The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.