A review on machinery diagnostics and prognostics implementing condition-based maintenance

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

Rotating machinery prognostics: State of the art, challenges and opportunities

Chatter is a self-excited vibration that can occur during machining operations and become a common limitation to productivity and part quality. For this reason, it has been a topic of industrial and academic interest in the manufacturing sector for many years. A great deal of research has been carried out since the late 1950s to solve the chatter problem. Researchers have studied how to detect, identify, avoid, prevent, reduce, control, or suppress chatter. This paper reviews the state of research on the chatter problem and classifies the existing methods developed to ensure stable cutting into those that use the lobbing effect, out-of-process or in-process, and those that, passively or actively, modify the system behaviour.

Chatter in machining processes: A review

Prognostic modelling options for remaining useful life estimation by industry

This paper presents an integrated fault diagnostic and prognostic approach for bearing health monitoring and condition-based maintenance The proposed scheme consists of three main components including principal component analysis (PCA), hidden Markov model (HMM), and an adaptive stochastic fault prediction model The principal signal features extracted by PCA are utilized by HMM to generate a component health/degradation index, which is the input to an adaptive prognostics component for on-line remaining useful life prediction The effectiveness of the scheme is shown by simulation studies using experimental vibration data obtained from a bearing health monitoring testbed

http://www.nt.ntnu.no/users/skoge/prost/proceedings/acc05/PDFs/Papers/0487_ThB15_2.pdf

An integrated approach to bearing fault diagnostics and prognostics

A novel fault diagnostics and prognostics algorithm based on hidden Markov model (HMM) is proposed. The algorithm combines fault diagnostics and prognostics in a unified framework. The algorithm has been fully tested by using experimental data from a rotating shift testbed in our laboratory.

A novel approach to fault diagnostics and prognostics

Modeling and intellligent chatter control strategies for a lathe machine

In this paper, we summarise our recent results in fault tolerant formation control of Unmanned Air Vehicles (UAV). A fault tolerant control scheme to deal with both GPS sensor failure and wireless communication packet losses is presented. Moreover, some extensions to the formation control algorithms developed by UC Berkeley are made to support time-varying heading and curved flight trajectories. The effectiveness of the presented fault tolerant control scheme is illustrated by real-time formation flight simulations conducted in a wireless network environment. Both rotary and fixed wing UAVs, mesh and triangular formations, and straight and curved trajectories are considered in our real-time simulations.

Fault tolerant formation flight control of UAVs

We propose two advanced control algorithms, the LMS adaptive filter and fuzzy CMAC neural network, to counteract the chatter problem for a lathe machine. Experimental results are also included. Approximately 20 dB reduction in chatter has been achieved. We have also developed a multi-DSP board which can be used to implement any type of intelligent controllers to machine systems. Other potential applications of the proposed methods are to milling and boring machines.

Modeling and intelligent chatter control strategies for a lathe machine

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