Acoustic Condition Monitoring Research Articles

Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities

Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities

Acoustic condition monitoring of coffee beans, during the roasting process

The research results presented here deal with the acoustic condition monitoring of coffee beans during the roasting process. In particular, the question was investigated whether the current state (roasting level) of the coffee beans during the roasting process can be detected by acoustic monitoring. To answer this question, an experiment was carried out with a sample drum roaster (Hottop KN–8828B– 2K+) in which the airborne sound pressure was recorded for five different roasting methods and then examined. The methods used differed in the inner temperature of the roaster when the coffee beans were inserted (75◦C, 100◦C, 150◦C, 175◦C, 200◦C). The recording of the roasting processes has been made by two microphones placed in front of the drum, with each process being repeated five times. To enable a comparison between the different processes, five points at which the coffee beans had reached a certain temperature were determined for each grating. Five sequences of equal length were then cut from each recording, with the determined time points serving as the starting value of the sequence.First, the differences in the spectral composition of the signals in the first and second crack were investigated and compared with those in other past publications. Furthermore, the comparison of the behavior as a function of the throw-in temperature has been investigated. For most of the processes analysed, the sound pressure levels can be clearly separated as a function of the bean temperature. In conclusion, it was shown that sound analysis can be used to determine the proportion of cracked beans.

Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring.

In the condition monitoring of bearings using acoustic emission (AE), the restriction to solely instrument one of the two rings is generally considered a limitation for detecting signals originating from defects on the opposing non-instrumented ring or its interface with the rollers due to the signal energy loss. This paper presents an approach to evaluate transmission in low-speed roller bearings for application in passive ultrasound monitoring. An analytical framework to describe the propagation and transmission of ultrasonic waves through the geometry and interfaces of a bearing is presented. This framework has been used to evaluate the transmission of simulated damage signals in an experiment with a static bearing. The results suggest that low- to mid-frequency signals (<200 kHz), when passing through the rollers and their interfaces from one raceway to the other, can retain enough energy to be potentially detected. An average transmission loss in the range of 10–15 dB per interface was experimentally observed.

Fault detection of contactor using acoustic monitoring

Recently, condition monitoring methods using the sound of the machine have attracted attention. Since approaching high voltage equipment increases the risk of electrocuting, non-contact data acquisition is desirable. Most of the research targets of acoustic monitoring are rotating machines and it is not clear whether it is effective for machines that switch between two states, such as contactors and circuit breakers. In this work, several investigations have been carried out on the acoustic condition monitoring of contactor. The Mel-frequency cepstrum coefficients (MFCCs) were obtained from the sound data of the contactors under normal and simulated fault conditions. Support Vector Machine (SVM) was trained with MFCCs and found that it could detect and diagnose contactor faults with high accuracy.

Acoustic Emission-Based Condition Monitoring and Remaining Useful Life Prediction of Hydraulic Cylinder Rod Seals

The foremost reason for unscheduled maintenance of hydraulic cylinders in industry is caused by wear of the hydraulic seals. Therefore, condition monitoring and subsequent estimation of remaining useful life (RUL) methods are highly sought after by the maintenance professionals. This study aimed at investigating the use of acoustic emission (AE) sensors to identify the early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF) in a test rig. In this study, the impact of sensor location and rod speeds on the AE signal were investigated using both time- and frequency-based features. Furthermore, a frequency domain analysis was conducted to investigate the power spectral density (PSD) of the AE signal. An accelerated leakage initiation process was performed by creating longitudinal scratches on the piston rod. In addition, the effect on the AE signal from pausing the test rig for a prolonged duration during the RTF tests was investigated. From the extracted features of the AE signal, the root mean square (RMS) feature was observed to be a potent condition indicator (CI) to understand the leakage initiation. In this study, the AE signal showed a large drop in the RMS value caused by the pause in the RTF test operations. However, the RMS value at leakage initiation is seen to be a promising CI because it appears to be linearly scalable to operational conditions such as pressure and speed, with good accuracy, for predicting the leakage threshold.

Automated and Rapid Seal Wear Classification Based on Acoustic Emission and Support Vector Machine

Seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In literature, successful attempts have been made using acoustic emission-based condition monitoring to classify the seal wear. However, limited attempts have been made to implement automated approaches to classify seal wear using acoustic emission features. Therefore, this article presents an automated approach for rapid and computationally economical diagnosis of seal wear using acoustic emission. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions similar to that of hydraulic cylinders. From the acoustic emission spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X downsampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A set of time and frequency domain features were designed, and the Support Vector Machine (SVM) was trained on 60 % of the test cases generated with seals of three different conditions. The SVM was able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99 %. Therefore, the proposed automated seal wear classification technique based on acoustic emission and SVM can be used for real-time monitoring of seal wear in hydraulic cylinders.

Defining acoustic emission-based condition monitoring indicators for monitoring piston rod seal and bearing wear in hydraulic cylinders

Fluid leakage from hydraulic cylinders is a major concern for the offshore industries as it directly affects hydraulic cylinder energy efficiency and causes environmental contamination. There have been attempts made in literature to develop robust condition monitoring techniques for hydraulic cylinders. However, most of these studies were performed to identify degradation of single components. Therefore, in this study, the aim is to monitor degradation of multiple components simultaneously in hydraulic cylinders using acoustic emissions. Experiments performed consist of three test phases and were performed using a hydraulic test rig. In the first test phase, the study is performed to identify acoustic emission features that can be used to monitor piston rod seal wear. In the second test phase, acoustic emission features are identified that can be used to understand bearing wear when unworn, semi-worn or worn piston rod seals are used in hydraulic test rig. In the third test phase, a run-to-failure test is conducted to identify acoustic emission features that can indicate fluid leakage initiation due to piston rod seal wear. The median frequency feature showed good repeatability in all the three test phases to identify piston rod seal wear, bearing wear and fluid leakage initiation during the initial stages in the hydraulic test rig. The proposed acoustic emission-based condition monitoring technique is robust and can be used for the hydraulic cylinders in the industries, as it identifies acoustic emission features based on particular frequency bands associated to specific components, making it less susceptible to noise from other components.

Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring

Wind energy is considered as one of the most important renewable energies in the world, employing larger and more complex wind turbines. They need novel condition monitoring systems to ensure the reliability, availability, safety and maintainability of the main components of the wind turbines. It leads to early fault detection, increasing the productivity and minimizing the maintenance costs and downtimes. This article proposes a novel non-destructive testing system to analyse acoustically rotatory devices of wind turbines. It captures the noise emitted by the devices using an acoustic condition monitoring system embedded in an unmanned aerial vehicle. The signal acquired is sent to ground computer station for recording and analysing the data. It uses a test rig, previously validated, to carry out a set of experiments to simulate the main faults. A signal processing method is done by wavelet transforms that filters and analyses the energy patterns of the signals. The results are analysed qualitatively and quantitatively considering different scenarios. A statistical analysis is developed to compare the numerical results provided by different wavelet transform families and convolutional neural network. It is concluded that Symlets and Daubechies families report equivalent results for this case study. The accuracies of the results are more than 75%, reaching up to 100%. The approach is validated employing Friedman test.

Acoustic emission based condition monitoring study of piston rod seals by varying speed and pressure parameters

Hydraulic cylinders are used in a wide range of applications such as oil drilling equipment, construction vehicles and manufacturing machines. Seal failure is one of the primitive causes of failure in hydraulic cylinders, possibly leading to fluid spill, unscheduled maintenance, reduced availability and thus leading to lower productivity. Regular visual inspection of seals without affecting the productivity is difficult as the seals are placed internally in the hydraulic cylinder requiring disassembly of the piston. Therefore, condition monitoring is required to assess the current health of the seals. There have been successful attempts made in literature for the assessment of seal quality using acoustic emission-based condition monitoring. However, there have been very few studies performed to diagnose the seal failure under varying speed and pressure parameters. Therefore, this study aims at increasing the understanding of seal failure under varying speed and pressure conditions through correlation with the acoustic emission signal. Experiments were performed on a hydraulic test rig using unworn, semi-worn and worn piston rod seals. For each seal wear condition, experiments were performed for five strokes at pressure conditions of 10, 20, 30 and 40 bar and speeds of 50 mm/s and 100 mm/s. Continuous acoustic emission data were acquired during all the tests. The acoustic emission signal of each piston rod stroke was analyzed using different acoustic emission features such as power spectral density, root mean square, peak, mean frequency, median frequency and band power. From the acoustic emission analysis, by using power spectral density, mean frequency and median frequency feature it is possible to identify and segregate unworn seal, leakage due to semi-worn seal and leakage due to worn seal in the test rig. The acoustic emission-based condition monitoring methodology developed in this study lays a strong foundation for further research to develop real-time monitoring of the piston rod seal in hydraulic cylinders that are used in the offshore industry.

Diagnostics of seal and rod degradation in hydraulic cylinders using acoustic emissions

External leakage from hydraulic cylinders is of a major concern for the offshore oil and gas industry. This occurs mainly as a result of physical damage to the piston rod or due to degradation of the piston rod seals. Numerous studies have been conducted to diagnose leakage from hydraulic cylinders due to seal failure using fluid, pressure, accelerometer or acoustic emission-based condition monitoring techniques. However, very few attempts have been made to diagnose multiple faults in hydraulic cylinders at the same time. Therefore, in this study, acoustic emission-based condition monitoring technique is used to detect and separate acoustic emission features due to different faults that are observed in hydraulic cylinders. An experimental study was performed on a test rig using water glycol as hydraulic fluid. Experiments were performed with different combinations of seals (unworn, semi worn and worn seals), and piston rods (unworn and worn). Acoustic emission features such as root mean square, peak, mean, kurtosis, skewness, mean frequency, median frequency and bandpower were used to identify seal and rod conditions. By using acoustic emission median frequency and mean frequency features it was possible to detect and separate leakage, and seal and rod degradation in the test rig over a large range of hydraulic working pressures. This study indicates that acoustic emission monitoring can be a strong basis for future research to identify and segregate other types of faults that are observed in hydraulic cylinders.

Flexural cracking-induced acoustic emission peak frequency shift in railway prestressed concrete sleepers

A novel technique for acoustic emission-based condition monitoring of railway prestressed concrete sleepers under flexural loading is established. The evolution of peak frequency of emissions under increasing loads is studied using data from four emission sensors. It is found that the bulk of emitted peak frequencies are clustered in three bands - [150–300] kHz, [300–460] kHz and [500–800] kHz in all cases of full-scale sleepers tested. Only slight variations of band ranges are observed. A correspondence of acoustic emission counts to a particular peak frequency is established. Not all emissions are damage-induced – most of them have relatively small number of counts, suggesting that those are due to random noise. Thus, it is proposed to filter out the non-significant peak frequencies with the least number of counts by applying a universal threshold rule. The largest proportion of emission counts corresponds to mid-span of the sleepers. It is shown that other acoustic emission sources exhibit a nearly linear shift in maximum values of peak frequency with increasing distance from this largest concentration of acoustic emission events. This novel insight into condition-based acoustic emissions is critical to develop a suitable and efficient technique for monitoring safety-critical railway sleepers and bearers located in a discreet and remote area. It will truly enable preventative, predictive and condition-based track maintenance for railway industry, minimizing cost and environmental impacts.

Towards acoustic condition monitoring for detection and characterisation of laser-induced breakdown in a gas turbine laser ignition system

Acoustic detection and characterisation of laser-induced breakdown is an attractive proposition in laser ignition systems in which condition monitoring is necessary but where optical access for monitoring purposes is impractical. This paper presents a signal processing method based on wavelet decomposition for the non-invasive detection of acoustic emissions resulting from laser-induced breakdown in an atmospheric pressure combustion test-rig, representative of a single combustion chamber in a sub-15 MW industrial gas turbine. The probability and consistency of laser-induced breakdown is determined from the acoustic signal and used to characterise the operating conditions and identify abrupt and incipient or slowly developing faults.

Evaluation of the effect of speed and defect size on high-frequency acoustic emission and vibration condition monitoring of railway axle bearings

Evaluation of the effect of speed and defect size on high-frequency acoustic emission and vibration condition monitoring of railway axle bearings

Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing

This paper presents an acoustic emission-based method for the condition monitoring of low speed reversible slew bearings. Several acoustic emission (AE) hit parameters as the monitoring parameters for the detection of impending failure of slew bearings are reviewed first. The review focuses on: (1) the application of AE in typical rolling element bearings running at different speed classifications, i.e. high speed (>600rpm), low speed (10–600rpm) and very low speed (<10rpm); (2) the commonly used AE hit parameters in rolling element bearings and (3) AE signal processing, feature extraction and pattern recognition methods. In the experiment, impending failure of the slew bearing was detected by the AE hit parameters after the new bearing had run continuously for approximately 15 months. The slew bearing was then dismantled and the evidence of the early defect was analysed. Based on the result, we propose a feature extraction method of the AE waveform signal using the largest Lyapunov exponent (LLE) algorithm and demonstrate that the LLE feature can detect the sign of failure earlier than the AE hit parameters with improved prediction of the progressive trend of the defect.

The Doppler Effect based acoustic source separation for a wayside train bearing monitoring system

Wayside acoustic condition monitoring and fault diagnosis for train bearings depend on acquired acoustic signals, which consist of mixed signals from different train bearings with obvious Doppler distortion as well as background noises. This study proposes a novel scheme to overcome the difficulties, especially the multi-source problem in wayside acoustic diagnosis system. In the method, a time–frequency data fusion (TFDF) strategy is applied to weaken the Heisenberg׳s uncertainty limit for a signal׳s time–frequency distribution (TFD) of high resolution. Due to the Doppler Effect, the signals from different bearings have different time centers even with the same frequency. A Doppler feature matching search (DFMS) algorithm is then put forward to locate the time centers of different bearings in the TFD spectrogram. With the determined time centers, time–frequency filters (TFF) are designed with thresholds to separate the acoustic signals in the time–frequency domain. Then the inverse STFT (ISTFT) is taken and the signals are recovered and filtered aiming at each sound source. Subsequently, a dynamical resampling method is utilized to remove the Doppler Effect. Finally, accurate diagnosis for train bearing faults can be achieved by applying conventional spectrum analysis techniques to the resampled data. The performance of the proposed method is verified by both simulated and experimental cases. It shows that it is effective to detect and diagnose multiple defective bearings even though they produce multi-source acoustic signals.

Chromatic acoustic condition monitoring of transformers

Purpose – The purpose of this paper is to present an approach for continuous acoustic condition monitoring of transformers based on chromatic principles for abstracting information on individual acoustic events as well as secondary trends in the behaviour of the events. Design/methodology/approach – The potential benefits of condition monitoring of high-value transformer equipment are explored, and an approach based on chromatic information abstraction is illustrated and discussed. Findings – Tracking of large numbers of complex and variable individual acoustic events over time using a chromatic approach appears to offer a means for remote operators to evaluate mechanical transformer tap changer condition in a traceable manner. Originality/value – The condition monitoring is retrofittable and non-intrusive, and the approach may be applied generically for combining condition indicators for overall health-checking. A complex system behaviour may be operationally simplified without discarding the complexity.

Acoustic condition monitoring of wind turbines: Tip faults

As a significant and growing source of the world’s energy, wind turbine reliability is becoming a major concern. At least two fault detection techniques for condition monitoring of wind turbine blades have been reported in early literature, i.e. acoustic emissions and optical strain sensors. These require off-site measurement. The work presented here offers an alternative non-contact fault detection method based on the noise emission from the turbine during operation. An investigation has been carried out on a micro wind turbine under laboratory conditions. 4 severity levels for a fault have been planted in the form of added weight at the tip of one blade to simulate inhomogeneous debris or ice build up. Acoustic data is obtained at a single microphone placed in front of the rotor. Two prediction methods have been developed and tested on real data: one based on a single feature – rotational frequency spectral magnitude; and another based on a fuzzy logic interference using two inputs - spectral peak and rotational peak variation with time. Results show that the single spectral peak feature can be used to determine fault severity in ranges. The implementation of the fuzzy logic using a further input feature is shown to significantly improve the detection accuracy.

Estimating Mean Residual Life for a Case Study of Rail Wagon Bearings

This paper develops a prognostics model to estimate the Mean Residual Life of Rail Wagon Bearings within certain confidence intervals. The prognostics model is constructed using a Proportional Hazards approach, informed by imperfect data from a bearing acoustic monitoring system, and a failure database. The model supports prediction within a defined maintenance planning window from the time of receipt of the latest acoustic condition monitoring information. We use the model to decide whether to replace a bearing, or leave it until collection of the next condition monitoring indicators. The model is tested on a limited number of cases, and demonstrates good predictive capability. Opportunities to improve the performance of the model are identified, and the processes necessary and time required to build the model are described. Lessons learned from dealing with real field data will assist those interested in using prognostics to support maintenance planning activities.

Reduction of Doppler effect for the needs of wayside condition monitoring system of railway vehicles

Technology of acoustic condition monitoring of vehicles in motion is based on the assumption that diagnostically relevant information is stored in the acoustic signal generated by a passing vehicle. Analyzing the possibilities of increasing the effectiveness of condition monitoring of a passing vehicle with stationary microphones, it should be noted that the acoustic signal recorded in these conditions is disturbed with the disturbance resulting from the Doppler effect. Reduction of signal's frequential structure disturbance resulting from the Doppler effect allows efficient analysis of changes in frequential structure of recorded signals and as a result extraction of relevant diagnostic information related with technical condition of running gear of vehicle.This article presents a method for removal of signal's frequential structure disturbances related with relative move of vehicles and stationary monitoring station. For elimination of the frequential non-stationary of signals disturbance-oriented dynamic signal resampling method was used. The paper provides a test of two methods for defining the time course of local disturbance of signal's frequential structure: the method based on the Hilbert transform and the method of analytical description of signal's disturbance based on the knowledge of a phenomenon that causes frequential non-stationarity of signals.As an example, the results of the processing and analysis of acoustic signals recorded by wayside measuring station, during the passage of WM-15A railway vehicle on an experimental track of Polish Railway Institute, are presented.

On acoustic measurement-based condition monitoring of internal combustion engines

Original article can be found at: http://www.bindt.org/ Copyright British Institute of Non-destructive Testing [Full text of this article is not available in the UHRA]