Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Acoustic emission (AE) measurements are one of many non-destructive testing methods which had found applications in defects detection in machines. This paper reviews the state of the art in AE based condition monitoring with particular emphasis on rotating and reciprocating machinery applications. Advantages and limitations of the AE technique in comparison to other condition monitoring techniques in detecting common machinery faults are also discussed.

Vibration monitoring is widely recognized as an effective tool for the detection and diagnosis of incipient failures of gas turbines. This paper presents a review of vibration based methods for turbine blade faults. Methods typically involved analysis of blade passing frequencies, and extraction of dynamic signals from the measured vibration response. This includes frequency analysis, wavelet analysis, neural networks and fuzzy logic and model based analysis. The literature reviewed showed that vibration could detect most types of blade faults on the basis that dynamic signals are correctly extracted using the most appropriate signal processing method.

Additive manufacturing such as fused deposition modelling (FDM) three dimensional printing machine can deliver complex shapes with fine details in relatively short building time using various materials (albeit mostly polymers in this case). However, the quality of the printed results might vary depending on the set input. The present study aims to investigate the effects of part orientation on dimensional accuracy, part strength, total cost, and surface quality of a three dimensional printed part. Five test specimens were printed on FDM machine with polylactic acid (PLA) as the material by varying the build orientation, i.e. X0°Y0°, X90°Y0°, X0°Y90°, X0°Y45°, and X90°Y45°. Dimensional accuracy, surface roughness, and part strength were measured by using digital vernier calliper, universal testing machine, and portable surface roughness tester, respectively. The results show that part orientation influences printed part quality, especially in accuracy and surface roughness. No part orientation studied delivered all positive quality expected, but overall, X0°Y0° orientation resulted the highest quality in terms of dimensional accuracy, mechanical properties, and cost.