Estimation of Fine and Oversize Particle Ratio in a Heterogeneous Compound with Acoustic Emissions

Abstract

The final phase of powder production typically involves a mixing process where all of the particles are combined and agglomerated with a binder to form a single compound. The traditional means of inspecting the physical properties of the final product involves an inspection of the particle sizes using an offline sieving and weighing process. The main downside of this technique, in addition to being an offline-only measurement procedure, is its inability to characterise large agglomerates of powders due to sieve blockage. This work assesses the feasibility of a real-time monitoring approach using a benchtop test rig and a prototype acoustic-based measurement approach to provide information that can be correlated to product quality and provide the opportunity for future process optimisation. Acoustic emission (AE) was chosen as the sensing method due to its low cost, simple setup process, and ease of implementation. The performance of the proposed method was assessed in a series of experiments where the offline quality check results were compared to the AE-based real-time estimations using data acquired from a benchtop powder free flow rig. A designed time domain based signal processing method was used to extract particle size information from the acquired AE signal and the results show that this technique is capable of estimating the required ratio in the washing powder compound with an average absolute error of 6%.

Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission

“…With the recent advances in structural health monitoring (SHM) [2][3][4][5][6][7][8] and nondestructive evaluation (NDE) [9,10], many monitoring and evaluation methods, such as modal characteristics-based methods [11][12][13], active sensing [14][15][16][17][18][19][20], electromechanical impedance [21][22][23][24][25], ultrasonic guided wave [26], active thermography [26][27][28] and vibrothermography [29,30], among others, are available. Among them, acoustic emission technique [31][32][33][34][35][36][37] is an effective nondestructive technique that can characterize the wear process [38,39], and it has been widely used in civil engineering [40][41][42][43], mechanical engineering [44][45][46][47]<...>…”

Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

“…As mentioned, the proposed method applies a series of heuristically tuned linear thresholds of varying amplitudes where, for each amplitude region, the amplitude of the peaks are localised, from which a sub time‐series is formed [13–19]. That is to say, given signal , and a single amplitude threshold, a sub time‐series is formed: .…”

“…The main shortcoming of this signal processing method is based around the lack of frequency information offered as part of the decomposition, as it is purely a time-domain-based approach, and hence a further computation of the FFT would be necessary for applications where frequency information is relevant [13][14][15][16][17][18][19]. This is not deemed to be an immediate limitation for the case of the pattern recognition-based control scheme considered as part of this manuscript.…”

“…From the signal processing literature, Nsugbe et al. designed a time‐domain‐based algorithm applied for source separation of a mixing process using a resulting non‐stationary signal from the process [13–19]. This approach is a time‐domain‐based decomposition method that is centred around an iterative optimisation flow and uses heuristic reasoning and tuned linear amplitude thresholds as a basis for the signal separation process [13–21].…”

An Advanced Sensor for Particles in Gases Using Dynamic Light Scattering in Air as Solvent

“…Another type of particle analyzer uses the noise produced by falling particles on a metal sheet. The sound is captured by a microphone and analyzed, and details regarding particles can be obtained [ 8 ].…”