Laser triangulation (LT) is widely used in many fields due to its good stability, high resolution and fast speed. However, the accuracy in these applications suffers from severe constraints on the data acquisition accuracy of LT. To solve this problem, the optical triangulation principle, the object equation of the optical path relationship and the deviation of the laser spot centroid are applied to deduce a mathematical model. Therefore, the image sensor inclination errors can be quantitatively calculated, and the collected data are compensated in real time. Further, a threshold sub-pixel gray-gravity (GG) extraction algorithm is proposed; the gradient function and Gaussian fit algorithm are used to set thresholds to remove the impact of the spot edge noise area on the center location; and polynomial interpolation is employed to enhance the data density of the traditional GG method, thus improving the data acquisition accuracy of LT. Finally, the above methods are applied to on-machine measurement of the American Petroleum Institute (API) thread and the screw rotor, respectively. The experimental results prove that the proposed method can significantly improve the measurement accuracy of free-form curved surfaces using LT and that the improved laser spot center extraction algorithm is more suitable for free-form curved surfaces with smaller curvature and more uniform curvature changes.

The inconvenient loading and unloading of a long and heavy drill pipe gives rise to the difficulty in measuring the contour parameters of its threads at both ends. To solve this problem, in this paper we take the SCK230 drill pipe thread-repairing machine tool as a carrier to design and achieve a fast and on-machine measuring system based on a laser probe. This system drives a laser displacement sensor to acquire the contour data of a certain axial section of the thread by using the servo function of a CNC machine tool. To correct the sensor’s measurement errors caused by the measuring point inclination angle, an inclination error model is built to compensate data in real time. To better suppress random error interference and ensure real contour information, a new wavelet threshold function is proposed to process data through the wavelet threshold denoising. Discrete data after denoising is segmented according to the geometrical characteristics of the drill pipe thread, and the regression model of the contour data in each section is fitted by using the method of weighted total least squares (WTLS). Then, the thread parameters are calculated in real time to judge the processing quality. Inclination error experiments show that the proposed compensation model is accurate and effective, and it can improve the data acquisition accuracy of a sensor. Simulation results indicate that the improved threshold function is of better continuity and self-adaptability, which makes sure that denoising effects are guaranteed, and, meanwhile, the complete elimination of real data distorted in random errors is avoided. Additionally, NC50 thread-testing experiments show that the proposed on-machine measuring system can complete the measurement of a 25 mm thread in 7.8 s, with a measurement accuracy of ±8 μm and repeatability limit ≤ 4 μm (high repeatability), and hence the accuracy and efficiency of measurement are both improved.

In order to improve the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images, an image denoising method based on low-rank and sparse decomposition (LRSD) was proposed in this paper. Relative total variation (RTV) was introduced into the method on the basis of the weighted nuclear norm minimization (WNNM) model to construct a new RTV-WNNM model, so as to enhance the denoising accuracy and detail-preserving capability of LRSD method. Alternating direction multiplier method (ADMM) was used to solve this convex problem iteratively. Moreover, in view that the existing LRSD denoising method had insufficient detail-preserving capability, an adaptive selection method of LRSD singular value threshold based on the improved elitist clone selection algorithm (ECSA) was also raised in this paper. The constructed RTV-WNNM model taken as the objective function, the improved ECSA could realize equilibrium between search in depth and optimization in breadth through the adaptive chaotic mutation operator and antibody reselection strategy based on antibody concentration and antigen affinity moment. Furthermore, it could reach rapid self-adaption to acquire a low-rank threshold and then form an image denoising fusion algorithm, which had both denoising and detail-preserving capabilities. Simulation and experimental results show that the proposed method can reach better results in both quantity measure and visual quality than the state-of-the-art image denoising methods, and it has also remarkably improved the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images.

The vibration isolator equipped with a negative stiffness corrector (NSC) excels at vibration isolation, but its stiffness often presents complex nonlinearity which needs to be approximated in calculation. To avoid the harmful effects of approximate stiffness, the NSC formed by the cam-roller mechanism with a quadratic polynomial trajectory (QCRM) is proposed to construct the vibration isolation system. From the inherent geometrical relationship in the structure, the generation mechanism of high-static-low-dynamic stiffness is analyzed, and the quasi-zero stiffness (QZS) condition of the system is derived. Based on the dynamic model of the QZS vibration isolator, the functions of response characteristics are solved by the harmonic balance method. Then, the absolute displacement transmissibility with different parameter values, and the vibration isolation performance under sinusoidal, multi-frequency wave, and random excitations are discussed. The simulated results show that the stiffness expression of the proposed QZS vibration isolator is directly a quadratic function, which removes the calculation error caused by approximate stiffness at large displacement and broadens the available isolation displacement range. Introducing the QCRM-NSC can significantly suppress the low-frequency vibration and resonance response without changing the load-bearing capacity of the vibration isolator. Under various excitations, the vibration isolation performance of the QZS vibration isolator all outperforms the linear counterpart.

The fault features of rolling bearings are always difficult to detect due to the complex operating environment, especially when the fault is in the incipient stage with weak symptoms. For fault detection, this paper proposes a signal processing method based on cascaded combination top-hat product filter and Teager energy spectrum (CCTHPF-TES). First, combining the morphological top-hat operation and cascaded morphological operators, four cascaded combination morphological top-hat operators are presented for extracting the fault features in the vibration signal. Subsequently, by comprehensively investigating the filter characteristics of the proposed operators, a cascaded combination top-hat product operator (CCTHPO) is constructed to further highlight the fault feature information. Moreover, a high-order statistical analysis tool called TES is employed to further improve the detection ability of CCTHPO. Finally, an autocorrelation kurtosis criterion is defined to automatically determine the optimal output of CCTHPF-TES without prior knowledge of faults. Experimental data obtained from the bearing life-cycle test and engineering application are applied to testify the availability of CCTHPF-TES. The results show the superiority of the proposed CCTHPF-TES in bearing fault detection.