Strategies for Extracting Damage Induced AE Signals from Different Type Noise-Like Backgrounds for Carbon-Fibre Reinforced Polymers

This paper presents an algorithm for isolating a useful acoustic signal (corresponding to damage accumulation) against the background of a signal used to model the performance of an industrial rotary equipment. Acoustic emission signals induced by deformation and fracture were studied using a uniaxial tensile test on woven laminate samples cut along the fiber and weft directions. The background signal is a random composition of acoustic pulses used to model the performance of an industrial rotary equipment. A comparison of useful and noise signals enables us to develop two algorithms based on frequency filtering of a signal and its decomposition into empirical modes. These algorithms can be used to isolate useful AE pulses against the background of all signal intensities under consideration.

A Review of Ball Bearings Fault Size Estimation (FSE), Fault Degradation Estimation (FDE), and Artificial Intelligence Based Approaches during Prognosis

Ball bearings are critical components of any industrial rotary equipment. They constitute about 90% of industrial machines’ components – and are thus responsible for the largest proportion of failures – approximately 70-85% of downtime. Defected bearings, while in service, give rise to high vibration amplitudes in rotary equipment, resulting in great reduction in their operational efficiency coupled with high energy consumption. Their premature and inadvertent failure could result in unplanned equipment downtown – thereby causing production loss and increased maintenance cost. Patently, to curtail this, it is vital that their health state is monitored throughout their service life for early faults detection, diagnosis, and prognosis. A knowledge of when a bearing will fail – that is, its remaining useful life (RUL) – can serve as supplement to maintenace decision-making such as determining in advance the time an equipment needs to be taken out-of-service and that can alternatively allow for sufficient lead time for maintenance planning as well. This can correspondingly result in enhancement in rotary systems effectiveness – i.e., availability, reliability, maintainability, and capability. Three popular condition monitoring approaches are signal processing-based approaches namely fault size estimation (FSE) and fault degradation estimation (FDE) as well as artifial intelligent (AI) based approach. It is, however, still a challenge to estimate a bearing fault size and therefore its RUL with high precision based on what has been diagnosed using these approaches. Accordingly, this review holistically explore capabilities and limitations of these approaches from recently published work. The reviewed limations are summarized and serve as new research avenue.

Respon Vibrasi Overall dan Temperatur Komponen Mesin Terhadap Misalignment Axial

Misalignment is one of the problems that often occurs in rotary equipment. In this paper, the observation was done to the rotary machine model in response to changes of axial misalignment by using vibration meter and infrared thermography camera. The model was a machine series, consisting of electric motor and disc which was connected by 3 Jaw flexible coupling. The shafts were positioned into several axial misalignment conditions to see the effect on the overall vibration value and its component temperature. The result of vibration observation showed that under certain conditions, by increasing shaft misalignment then the overall vibration value tends to decrease. With the decrease of overall vibration value, the machine condition was not in better condition. This was indicated by observing the temperature of the components in this condition, which showed the opposite fact. As misalignment increases, the temperature of the engine components also increases.