Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

Elforjani, Mohamed; Shanbr, Suliman

doi:10.1109/tie.2017.2767551

Cited by 186 publications

(91 citation statements)

References 21 publications

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“…Acoustic signals have been widely used as a mean to closely monitor damages occurring over time in different systems [13,21]. During the mechanical testing in our research, an AE monitoring equipment was used to collect all AE generated during the internal pressurization test of the tubular samples, as described in [10,11].…”

Section: Datasetmentioning

confidence: 99%

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

et al. 2020

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Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

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Section: Datasetmentioning

confidence: 99%

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

et al. 2020

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“…Once the time step updates to time step k + 1, the true measured data at time step k, y k , is available, so the weight vector can be updated as and the true measurement y k into Eqs. (15)- (17). Thus the optimal hyper-parameters for time step k is obtained as:…”

Section: Online Reliability Prediction Stagementioning

confidence: 99%

“…Another trajectory to address the reliability assessment and SOH prediction resorts to posterior estimation methodologies, e.g. machine learning, which asserts system state and system reliability or SOH through a "black box" constructed upon massive historical CM data and current measurement [15][16][17]. Nevertheless, this kind of methods have not yet been explored in depth for online SOH prediction under the dynamic multi-state condition, because of the difficulties lying in three aspects: (1) how to identify different system state by CM signals, in other words, how to effectively select the feature from CM signals; (2) how to efficiently classify the selected features into classes and (3) how to dynamically adapt the "black box" -like prediction model to meet the realtime demand for online tasks.…”

Section: Introductionmentioning

confidence: 99%

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao

Zio

Zhao

2018

Reliability Engineering & System Safety

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Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.

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“…Among the various diagnostic methods, vibration analysis [9,10], motor current signature analysis (MCSA) [11], acoustic emission (AE) [12], and stray flux monitoring [13] are widely practiced. The vibration analysis is a traditional method, but parameters like cost, access, and placement of vibration sensors make it difficult to gain use in practical cases.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

Pandarakone

Mizuno

Nakamura³

2019

Energies

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Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.

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Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

Cited by 186 publications

References 21 publications

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

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