Search

Bearing component damage contributes significantly to rotating machinery failures. It is vital for the rotor-bearing system to be in good condition to ensure the proper functioning of the machine. Over recent decades, extensive research has been devoted to the condition monitoring of rotational machinery, with a particular focus on bearing health. This paper provides a comprehensive literature review of recent advancements in intelligent condition monitoring technologies for rolling element bearings. Fundamental monitoring strategies are introduced, covering various sensing, signal processing, and feature extraction techniques for detecting defects in rolling element bearings. While vibration-based monitoring remains prevalent, alternative sensor types are also explored, offering complementary diagnostic capabilities or detecting different defect types compared to accelerometers alone. Signal processing and feature extraction techniques, including time domain, frequency domain, and time–frequency domain analysis, are discussed for their ability to provide diverse perspectives for signal representation, revealing unique insights relevant to condition monitoring. Special attention is given to information fusion methodologies and the application of intelligent algorithms. Multisensor systems, whether homogeneous or heterogeneous, integrated with information fusion techniques hold promise in enhancing accuracy and reliability by overcoming limitations associated with single-sensor monitoring. Furthermore, the adoption of AI techniques, such as machine learning, metaheuristic optimisation, and deep-learning methods, has led to significant advancements in condition monitoring, yielding successful outcomes with improved accuracy and robustness in various studies. Finally, avenues for further advancements to improve monitoring accuracy and reliability are identified, offering insights into future research directions. [ABSTRACT FROM AUTHOR]

A study was conducted to investigate the passivity analysis and passification problems of a large scale of uncertain systems that have uncertain parameters, time delays, quantization errors and unmodeled high-order dynamics. To solve these problems, a linear matrix inequality-based solution was presented. Results of the study also indicated that passivity analysis is a useful tool in assessing the robust stability of signal processing systems involving nonlinear elements.

A study has been conducted to provide a linear matrix inequality (LMI) approach to the robust H(infinity) filtering problem for a general class of uncertain linear system described by the integral quadratic constraints (IQCs). The problem is integral in various signal processing applications. LMI is numerically efficient because of the recent advances in convex optimization. The research focuses both on the continuous and discrete-time uncertain linear system.

This paper presents an experimental study of the effects of cutting parameters of micro-end-milling process on the machined surface roughness, in order to find the optimal operation conditions for improved surface finish. Three types of metals, namely 6160 aluminium alloy, brass, and AISI 1040 steel, are used as work materials. The effect of material property on the surface roughness is investigated. It is found that under the same machining condition, the machined surface quality of aluminium alloy is the worst, while that of brass is the best. A multiple regression model for the surface roughness is developed, which includes the effects of cutting speed, feedrate, and the interaction between them. Results based on analysis of variance (ANOVA) show that the cutting speed is the most significant factor on surface roughness. The residual analysis indicates that the multiple regression model is valid and agrees with the experimental results. [ABSTRACT FROM PUBLISHER]

In this study, the authors present the research results on the robust investigate the robust l2–l∞ filtering for Markovian jump linear systems with multiple sensor faults, uncertain probability transition matrix and time‐varying delays. The multiple sensor faults are modelled as multiple independent Bernoulli processes with constant probabilities. The uncertain probability transition matrix is modelled via the polytopic uncertainties for each row in the transition matrix. By using the augmentation method, the filtering error system with stochastic variables is derived. Since of the stochastic variables, the traditional stability condition is not qualified for the analysis of the filtering error systems. Thus, the exponentially mean‐square stability and the robust l2–l∞ performance are adopted for the filtering error system. By choosing the Lyapunov‐based method, sufficient conditions which can guarantee the exponentially mean‐square stability and the robust l2–l∞ performance are obtained in the forms of matrix inequalities. Based on these conditions, the filter design method is proposed and the estimator parameters can be obtained by solving a set of linear matrix inequalities. Finally, a numerical example with two modes is used to show the design procedure and the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

Considers the robust H infinity filtering problem for a general class of uncertain linear systems described by the integral quadratic constraints (IQC). Importance of the problem in many signal processing applications; Integral quadratic constraints; Solution to the problem using linear matrix inequality.