Optimization of indentification of particle impacts using acoustic emission.

Air-borne or liquid-laden solid particle transport is a common phenomenon in various industrial applications. Solid particles transported at severe operating conditions such as high-flow velocity can cause concerns for structural integrity, through wear originating from particle impacts with the structure. To apply Acoustic Emission (AE) in particle-impact monitoring, previous researchers focused primarily on dry particle impacts on dry target plate and/or wet particle impacts on wet or dry target plate. For dry particle impacts on dry target plate, AE events energy - calculated from the recorded free-falling or air-borne particle impact AE signals - was correlated with particle size, concentration, height, target material and thickness. For a given system, once calibrated for a specific particle type and operating condition, this technique might be sufficient to serve the purpose. However, if more than one particle type is present in the system (particularly with similar size, density and impact velocity), calculated AE event energy is not unique for a specific particle type. For wet particle impacts on dry or wet target plate (either submerged or in a flow loop), AE event energy was related to the particle size, concentration, target material, impact velocity and angle between the nozzle and the target plate. In these studies, the experimental arrangements and operating conditions either did not account for any bubble formation in the system or, if they did, did so at least an order of magnitude lower in amplitude than the sand particle impact. In reality, bubble formation can be comparable with particle impacts in terms of AE amplitude in process industries such as sand production during oil and gas transportation away from a reservoir. Current practice is to calibrate an installed AE monitoring system against a range of sand-free flow conditions. In real-time monitoring for a specific calibrated flow, the flow-generated AE amplitude/energy is deducted from the recorded AE amplitude/energy and the difference is attributed to the sand particle impacts. However, if the flow condition changes, which it often does in the process industry, the calibration is no longer valid and AE events from bubbles can be misinterpreted as sand particle impacts, and vice versa. In this thesis, sand particles and glass beads (with similar size, density and impact velocity) have been studied, dropping from 200 mm using a small cylindrical stepped mild steel coupon as a target plate. For signal recording purposes, two identical broadband AE sensors are installed, one at the centre and one 30 mm off-centred, on the opposite side of the impacting surface. Signal analysis has been carried out by evaluating seven standard AE parameters (amplitude, energy, rise time, duration, power spectral density (PSD), peak frequency at PSD, and spectral centroid) in the time and frequency domain, and time-frequency domain analysis has been performed by applying Gabor Wavelet Transformation. The signal interpretation becomes difficult due to reflections, dispersions and mode conversions caused by close proximity of the boundaries. So, a new signal analysis parameter - frequency-band energy ratio - has been proposed. This technique is able to distinguish between the populations of two very similar (in terms of size, mass and energy) groups of sand particles and glass beads impacting on mild steel, based on the coefficient of variation (Cv) of the frequency-band AE energy ratios. To facilitate individual particle impact identification, further analysis has been performed using a Support Vector Machine (SVM)-based classification algorithm with seven standard AE parameters, evaluated in both the time and frequency domain. The available dataset has been segmented into two parts: training set (80%) and test set (20%). The developed model has been applied on the test data for the purpose of model performance evaluation. The overall success rate in individually identifying each category (PLB, Glass bead and Sand particle impacts) at S1 has been found as 86%, and at S2 as 92%. To study wet particle impacts on a wet target surface in the presence of bubbles, the target plate was sealed to a cylindrical perspex tube. Single and multiple sand particles were introduced in the system using a constant-speed blower, to impact the target surface under water-loading. Two sensor locations, the same as those used in the previous sets of experiments, were monitored. From frequency domain analysis, it has been observed that the characteristic frequencies for particle impacts are centred at 300-350 kHz, and the frequencies for bubble formations are centred at 135-150 kHz. Based upon this, two frequency bands - 100-200 kHz (E1) and 300-400 kHz (E3) - and the frequency-band energy ratio (E3/E1) have been identified as optimal for identifying particle impacts for the given system. E3/E1 > 1 has been associated with particle impacts and E3/E1 < 1 has been associated with bubble formations. By applying these frequency-band energy ratios and setting an amplitude threshold, an automatic event identification technique has been developed for identification of sand particle impacts in presence of bubbles. The method developed can be used to optimize the identification of sand particle impacts. The optimal setting of an amplitude threshold is sensitive to the number of particles and the noise levels. For example, a high threshold of 10% will clearly identify sand particle impacts, but for multiparticle tests the same threshold is unlikely to detect about 20% of lower energy particles. On the other hand, a threshold lower than 3% is likely to result in the detection of AE events with poor frequency content and incorrect classification of the weakest events. The optimal setting of the parameters used in the framework - such as thresholds, frequency bands and ratios of AE energy - is therefore likely to make identification of sand particle impacts in a laboratory environment possible within 10%. An additional advantage of this technique is that calibration of the signal levels is not required, once the optimal frequency bands and ratios have been identified