scholarly journals Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Liang Guo ◽  
Yingqi Huang ◽  
Hongli Gao ◽  
Li Zhang
Keyword(s):  
Fault Location ◽  
Frequency Estimation ◽  
Time Estimation ◽  
Rotational Frequency ◽  
Health Condition ◽  
Ball Screw ◽  
Rotating Machines ◽  
Time Frequency ◽  
Crucial Component ◽  
Instantaneous Rotational Frequency

Ball screw, as a crucial component, is widely used in various rotating machines. Its health condition significantly influences the efficiency and position precision of rotating machines. Therefore, it is important to accurately detect faults and estimate fault location in a ball screw system to make sure that the ball screw system runs safely and effectively. However, there are few research studies concerning the topic. The aim of this paper is to fill the gap. In this paper, we propose a method to automatically detect and locate faults in a ball screw system. The proposed method mainly consists of two steps: fault time estimation and instantaneous rotational frequency extraction. In the first step, a statistics-based outlier detection method is proposed to involve the fault information mixing in vibration signals and estimate the fault time. In the second step, a parameterized time-frequency analysis method is utilized to extract the instantaneous rotational frequency of the ball screw system. Once the fault time and instantaneous rotational frequency are estimated, the fault location in a ball screw system is calculated through an integral operation. In order to verify the effectiveness of the proposed method, two fault location experiments under the constant and varying speed conditions are conducted in a ball screw failure simulation testbed. The results demonstrate that the proposed method is able to accurately detect the faults in a ball screw system and estimate the fault location within an error of 22%.

scholarly journals RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Igor Djurović
Keyword(s):  
Instantaneous Frequency ◽  
Matching Pursuit ◽  
Frequency Estimation ◽  
Consensus Algorithm ◽  
Multidimensional Space ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Polynomial Phase ◽  
Fm Signals ◽  
Fifth Order

AbstractFrequency modulated (FM) signals sampled below the Nyquist rate or with missing samples (nowadays part of wider compressive sensing (CS) framework) are considered. Recently proposed matching pursuit and greedy techniques are inefficient for signals with several phase parameters since they require a search over multidimensional space. An alternative is proposed here based on the random samples consensus algorithm (RANSAC) applied to the instantaneous frequency (IF) estimates obtained from the time-frequency (TF) representation of recordings (undersampled or signal with missing samples). The O’Shea refinement strategy is employed to refine results. The proposed technique is tested against third- and fifth-order polynomial phase signals (PPS) and also for signals corrupted by noise.

scholarly journals A Joint Method Based on Time-Frequency Distribution to Detect Time-Varying Interferences for GNSS Receivers with a Single Antenna

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1946 ◽  
Author(s):  
Qingshui Lv ◽  
Honglei Qin
Keyword(s):  
Hough Transform ◽  
Frequency Estimation ◽  
Satellite System ◽  
Time Varying ◽  
Actual Performance ◽  
Time Frequency ◽  
Single Antenna ◽  
Global Navigation Satellite ◽  
Mean Square Errors ◽  
Joint Method

In this paper, a joint method combining Hough transform and reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) is presented to detect time-varying interferences with crossed frequency for a Global Navigation Satellite System (GNSS) receiver with a single antenna. The proposed method can prevent the cross-term interference and detect the time-varying interferences with crossed frequency which cannot be achieved by the classical time-frequency (TF) analysis with the peak detection method. The actual performance of the developed method has been evaluated by experiments with conditions where the real BeiDou system (BDS) B1I signals are corrupted by the simulated chirp interferences. The results of experiments show that the introduced method is effectively able to detect chirp interferences with crossed frequency and provide the same root mean square errors (RMSE) of the parameter estimation for chirp one and the improved initial frequency estimation for chirp two compared with the Hough transform of Wigner-Ville distribution (WVD) when the jamming to noise ratio (JNR) equals or surpasses 4 dB.

Non-dominated solution set based on time–frequency infograms for local damage detection of rotating machines

2019 ◽  
Vol 92 ◽  
pp. 213-227 ◽  
Author(s):  
Xingxing Jiang ◽  
Juanjuan Shi ◽  
Weiguo Huang ◽  
Zhongkui Zhu
Keyword(s):  
Damage Detection ◽  
Solution Set ◽  
Local Damage ◽  
Rotating Machines ◽  
Time Frequency

scholarly journals Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV

2019 ◽  
Vol 9 (23) ◽  
pp. 5154
Author(s):  
Rachele Anderson ◽  
Peter Jönsson ◽  
Maria Sandsten
Keyword(s):  
Stochastic Model ◽  
Frequency Estimation ◽  
Optimal Time ◽  
Model Parameters ◽  
Inference Method ◽  
Physiological Factors ◽  
Time Frequency ◽  
Spectral Estimates ◽  
Novel Approach ◽  
Spectral Estimator

In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants’ state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences.

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