A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

Most gear fault diagnosis(GFD) approaches su er from ine ciency when facing with multiple varying working conditions at the same time. In this paper, a non-negative matrix factorization(NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to o er a fast multi-tasking solution. The short-time Fourier transform(STFT) is first used to obtain the time-frequency features from the gear vibration signal. Then, the optimal clustering numbers are estimated using the Bayesian information criterion(BIC) theory, which possesses the simultaneous assessment capability, compared with traditional validity indexes. Subsequently, the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks. Finally, the parameters involved in BIC and NMF algorithms are determined using the gradient ascent(GA) strategy in order to achieve reliable diagnostic results. The Spectra Quest's Drivetrain Dynamics Simulator gear data sets were analyzed to verify the e ectiveness of the proposed approach.     

Real-time administration of tool sharing and best matching to enhance assembly lines balanceability and flexibility

Collaboration has been found in previous studies on the design of assembly lines to be a useful mechanism. In this study, the focus is on a collaborative assembly (CA) framework, inspired by the design principles of CCT, the Collaborative Control Theory, to improve balanceability and flexibility of assembly lines through tool sharing (TS) among idle and bottleneck workstations. TS is widely practiced in advanced assembly facilities to reduce cost and improve consistency and standardization in assembly and in assembly-and-test utilities, relying often on real time control. The framework developed here addresses the systems design aspect of Mechatronics, covering the planning, execution, and control mechanisms. Planning includes assembly line balancing (ALB) and initial TS decisions, made continually by solving a bi-objective mixed-integer program (BOMIP). A collaborative multi-agent system (CMAS) enhanced with a TS-best matching (BM) protocol is developed to execute the plan, control the process, and modify the TS decisions, considering dynamic changes in the system’s operations. Experiments show that the new CA framework significantly outperforms classic approaches (i.e., ALB without TS-BM) in terms of cycle time, utilization of tools, and balanceability. In addition, the control mechanism is proven to augment the line’s flexibility against the inherent uncertainties of assembly processes, compared to the previously developed static CA frameworks.     

Tuning of Active Disturbance Rejection Control with application to power plant furnace regulation

Active Disturbance Rejection Control (ADRC) is emerging as a promising solution in dealing with the unmeasurable disturbances and unknown uncertainties, which are treated in a lumped manner and augmented as an extended state variable. Subsequently, an extended state observer (ESO) is designed to estimate and cancel the combined uncertain term in real time, modifying the uncertain plant to behave like a nominal model consisting of integrators. In the original ADRC formulation, the plant model is assumed to be of delay-free and its order is assumed to be equal to that of the real plant. However, a low-order ADRC is preferred and received a wide acceptance in practice because of its simplicity. Currently, the feasibility of such practice is not clearly revealed as well as its potential dangers. To this end, this paper analyzes the control mechanism from the perspective of the modified plant, which, in turn, would give guidance to parameter tuning. The effect of each parameter on the compensation efficiency and stability conditions of the modified plant is analyzed, based on which a complete tuning procedure for ADRC is developed where the initial settings is derived from the existing PI controller parameters. Finally, the proposed tuning method is experimentally used for a furnace pressure regulation of a 1000MW power plant, validating the feasibility of the low-order ADRC, even in the absence of both dynamic model and the information on the model order.     

A tutorial – game theory-based extended H infinity filtering approach to nonlinear problems in signal processing

In this paper, we provide a tutorial for the applications of “game-theory-based extended H infinity filtering (EHIF)” approach to various problems in disciplines of signal processing. The algorithm of this filtering approach is similar to that of the extended Kalman filtering (EKF). Since its invention, the Kalman filtering approach has been successfully and widely employed for many problems in scientific and engineering fields, e.g. target tracking, satellite systems, control, communications, etc. Therefore, the H infinity filtering approach also can be applied to all these problems. One big difference of EHIF from the EKF approach is that we apply it with unknown noise statistics of the state and measurement. In this tutorial, we introduce this non-well-known approach in spite of its practical usefulness, by providing the step by step algorithm with example problems of a number of signal processing disciplines. We also show that EHIF can outperform other approaches including the EKF that need to know the noise statistics in their applications, in some scenarios. By the contribution of this tutorial, we look forward to easy, and disseminative applications of EHIF to problems where, particularly, the EKF or particle filter could have been applied if noise statistics were known.     

Four-year study of roughness patterns using weekly calibrations

In order to evaluate the stability and reproducibility of roughness measurements, calibrations of the same roughness standard were held over four years. Calibrations were performed in the Laboratório de Medição de Superfícies Ópticas (LMSO), on Mondays, between late January to early December every year. Here are presented and analyzed the calibration results and also the measurements of lab temperature and relative humid, at the time of calibration, in order to assess if there is any correlation between these influence factors in the obtained results. The LMSO lab is accredited by INMETRO (Brazilian NMI).     

Feature extraction and soft computing methods for aerospace structure defect classification

This study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one; and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method – namely the novel CBIR-based one outperforms all the competitors – and they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application.     

Resolver angle estimation using parameter and state estimation

In this paper, a new type of a resolver angle estimator that utilizes a combined parameter and state estimation scheme is proposed. A state-space model of a resolver is first developed with unknown parameters. Least square estimation is employed to obtain some unknown model parameters by using the measurements up to the current time. Based on the state-space model with estimated parameters, a constrained state estimator with finite memory is constructed to estimate the resolver angle. It is shown through simulation that the proposed scheme is very effective in suppressing noise and overcoming amplitude and phase imbalances compared with common angle tracking observers.     

Vibration component separation by iteratively using stochastic resonance with different frequency-scale ratios

It is well-known that stochastic resonance (SR) is mainly used for signal denoising and weak signal detection. In this paper, we firstly find the frequency range selection characteristic (filtering characteristic) of re-scaling frequency SR (RFSR) caused by the driving frequency limitation of bistable SR. It then follows that a novel approach to separate vibration components with different frequencies by iteratively using SR is explored. The frequencies of most vibration signals exceed the driving frequency limitation, thus by use of different frequency-scale ratios, the vibration signals with different frequency range can be extracted by RFSR. Firstly, a small frequency-scale ratio is used to obtain the vibration signal with a narrow frequency range, i.e. low frequency vibration. As the output of SR may have a phase lag, a simple phase-shift correction method is proposed to improve the accuracy of signal component separation. The phase-shift corrected signal of RFSR output is separated from the original vibration signal and the residue is treated as the new vibration signal. Then, increasing the frequency-scale ratio according to a searching algorithm, the vibration signal with higher frequency can be obtained by RFSR. Through this iterative process, several harmonic vibration components can be separated from the original noisy vibration signal. The proposed method, empirical mode decomposition (EMD) and Hilbert vibration decomposition (HVD) are respectively applied to analyzing a simulated vibration signal and extracting the fault feature of a rotor system. The contrastive results show that this proposed method has good frequency resolution and can successfully separate monocomponent harmonic signals from a strongly noisy multicomponent harmonic vibration signal while EMD and HVD cannot.     

Methodology of improvement of radiometric quality of images acquired from low altitudes

Low-altitude photogrammetry studies have been more and more popular in the mapping of small areas (up to 10 thousand hectares). UAV flights can be consider as an attractive low-cost alternative solution for the photogrammetric studies. However, in this type of platforms images are frequently captured by digital compact cameras. Despite high resolution, the images taken with these cameras have a relatively low radiometric quality. While photogrammetric software for processing images obtained via sensors mounted on UAVs and the possible applications of the GPS RTK system for determining projection centres are constantly developing, the majority of studies nowadays still require digital aerial triangulation based on transferring and measuring tie points on subsequent images of the same surface fragment. A method for improving the quality of low-altitude image data is presented in this article. In order to improve the image radiometry, filtration in frequency domain was applied. This solution made it possible to enhance the reflection from objects in the images and at the same time reduce the impact of poor lighting on local contrast. The proposed method comprises two variants of radiometric correction, each of these depending on the quality of the pictures. The effectiveness of the method has been proven by adjusting three image blocks with different levels of radiometric quality before and after filtration, as well as a comparative analysis of the aerial triangulation results.     

Measurement on the structural,morphological, electrical and optical properties of PANI-CSA nanofilms

Camphorsulfonic acid doped polyaniline (PANI-CSA) prepared by chemical oxidative polymerization is spin coated on glass plates with four different PANI:CSA weight ratios (1:1, 1:2, 1:4 and 1:8) and measurements on the structural and optical properties are done. Thickness of the films measured <100 nm are termed as nanofilms. Fourier transform infrared spectroscopy indicated the presence of dopant and an increase in degree of polymerization with increase in dopant ratio. X-ray diffraction studies revealed the change of amorphous nature of the film to crystalline nature with increase in CSA dopant ratio. Scanning electron microscopy showed the change of very smooth morphology of the film to rough root-like morphology with increase in CSA dopant ratio. Hall-effect analysis showed that the increase in CSA weight ratio appreciably increases the conductivity of PANI-CSA films due to increase in carrier concentration and it also represents the semiconductivity (P-type) nature in all the films. UV–visible absorption studies reveal the broadening of absorption spectrum in visible region with maximum CSA dopant ratio (1:8). Photoluminescence spectra of PANI-CSA films excited using 300 nm, revealed that the change in intensity and position of emission peaks are due to transition of semiconducting nature of the film to conducting nature with an increase in CSA dopant ratio from 1:1 to 1:8.