Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the d...

The concept that changes in the dynamic behaviour of a rotor could be used for general fault detection and monitoring is well established. Current methods rely on the response of the machine to unbalance excitation and are mainly based on pattern recognition approaches. However these methods are relatively insensitive and the crack must be large before it can be robustly detected. Active magnet...

Eccentricity detection is a crucial issue for highspeed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and realtime RFID-based eccentricity detection...

Condition-based maintenance (CBM) presently plays an important role in avoiding unexpected failures, improving machine reliability, and providing accurate maintenance records and activities for rotating machinery. Traditional wired sensors commonly used for gathering data are costly and of limited value in industry. Recently, together with the advancement of sensor technology and communication ...

In this paper, two Piezo-Based Rotating Inertial Actuators (PBRIAs) are considered for the suppression of the structure-borne noise radiated from rotating machinery. As add-on devices, they can be directly mounted on a rotational shaft, in order to intervene as early as possible in the transfer path between disturbance and the noise radiating surfaces. generate the appropriate actuation signals...

In this paper, a new neural network (NN) for fault diagnosis of rotating machinery which synthesises the theory of adaptive resonance theory (ART) and the learning strategy of Kohonen neural network (KNN), is proposed. For NNs, as the new case occurs, the corresponding data should be added to their dataset for learning. However, the ‘off-line’ NNs are unable to adapt autonomously and must be re...

Various diagnostics methods have been applied to machinery condition monitoring and fault diagnosis, with far from satisfactory levels of accuracy. With the development of modern multi-sensor based data acquisition technology often used in advanced signal processing, more and more information is becoming available for the purposes of fault diagnostics and prognostics of machinery integrity. It ...

Abstract Vibro-acoustic signatures are widely used for diagnostics of rotating machinery. Vibration based automatic diagnostics systems need to achieve a good separation between signals generated by different sources. The separation task may be challenging, since the effects of the different vibration sources often overlap. In particular, there is a need to separate between natural frequencies ...

Traditionally, active control of structural radiators has approached the problem through integrating sensors and actuators on or into the structure. In such an approach, the goal is to modify the systems response to disturbance, rather than influence the disturbance path to the radiating structure. Many practical issues exist, which ultimately limit the effectiveness of such an approach. A gene...