Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Acoustic emission signal processing framework to identify fracture in aluminum alloys

Acoustic emission (AE) is a common nondestructive evaluation tool that has been used to monitor fracture in materials and structures. The direct connection between AE events and their source, however, is difficult because of material, geometry and sensor contributions to the recorded signals. Moreover, the recorded AE activity is affected by several noise sources which further complicate the identification process. This article uses a combination of in situ experiments inside the scanning electron microscope to observe fracture in an aluminum alloy at the time and scale it occurs and a novel AE signal processing framework to identify characteristics that correlate with fracture events. Specifically, a signal processing method is designed to cluster AE activity based on the selection of a subset of features objectively identified by examining their correlation and variance. The identified clusters are then compared to both mechanical and in situ observed microstructural damage. Results from a set of nanoindentation tests as well as a carefully designed computational model are also presented to validate the conclusions drawn from signal processing

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

Acoustic Emission Signal Denoising of Bridge Structures using SOM Neural Network Machine Learning

Identification Noise signal is one of the challenging problems in the health monitoring of bridge structure using acoustic emission monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common method in identifying of the signals from the defect of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focus on the AE noise signal from a bridge in operation state and other specific loading state, which is diagnosed in the hardware filtering technology, spatial identification and SOM neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50 %, and can barely filter strong noise signal. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90 % and 100 % respectively. The trained network is used to test183 sample signals, the defect signal detection accuracy reaches 76 % and 78.8 %, therefore, the noise signal filtering effect is significantly improved

Acoustic emissions: diagnosing tribological phenomenon in artificial joint materials

Studies have shown that many reported causes of failure of artificial joints such as hip, knee and spine are wear and friction related. Current modes of diagnosing failed artificial joints involve the use of imaging techniques like X-rays and CT scans, which although effective, are costly, time-consuming and harmful to patient health due to frequent exposure to radiation. There is the added limitation of the delay experienced before signs of failure become visible, causing further discomfort to the patient and, at times, health complications resulting from possible migration of wear debris into blood tissues. These complications have necessitated the need for a simpler and more dynamic system for identifying and diagnosing failed artificial joints, which is where the acoustic emission (AE) testing has shown promise. AE testing is a non-destructive test method used to detect the onset and progression of mechanical flaws that has proven advantageous in the analysis and understanding of tribological interactions in mechanical systems. In recent times, it has been increasingly used in the study of the tribology of artificial and natural human joints thereby showing potential as a tool for the identification and diagnosis of failed artificial joints. Thus, this research aimed to use AE to monitor the tribological characteristics of artificial joint materials as a first step toward using AE to diagnose artificial and natural joint pathologies. To gain an initial understanding of how AE features can be related to tribological mechanisms such as friction, in particular, a bio-tribo-acoustic tests system was developed. This enabled the acquisition of AE signals during biotribological testing of artificial joint materials. This proof-of-concept study showed that time-dependent (TDD) AE features can be used to predict the friction profile of a simulated polymer-metal artificial joint articulation. The prediction was carried out using a Non-linear Auto Regression with Exogeneous inputs (NARX) model. During testing of the trained model, predicted data had R2 values of 94% in tests on PEEK reciprocating at 2 Hz test and 98.6% for UHMWPE at 2 Hz. These regression results support the hypothesis that AE TDD features can be used to predict the friction profile which can then be related to the wear behaviour of the simulated joint articulation. Having proved the potential of AE as a biotribological diagnostic tool, the next step is to be able to use the acquired AE signals to identify the perceived damage mode prompting the need for a method by which AE signals can be differentiated according to different wear mechanisms. To this end, AE signals from adhesive and abrasive wear, simulated under controlled joint conditions, were classified using supervised learning. Principal component analysis was used to derive uncorrelated AE features and then classified using three methods – logistic regression, k-nearest neighbours and back propagation (BP) neural network. The BP network emerged as the best performing network with a classification accuracy of 98%. One of the limitations of traditional artificial neural networks (ANN) such as the BP network is the complex feature engineering required to obtain a model with high accuracy and high sensitivity. To mitigate this, deep transfer learning, with GoogLeNet as the base convolutional neural network (CNN) model, was used to classify AE signals from simulated damage mechanisms observed in retrieved polyethylene inserts of failed knee implants - burnishing and scratching wear. It was found that using CNN to extract features to be trained with an SVM model obtained a higher classification accuracy (99.3%) than just training with CNN model (96.5%). The work presented in this thesis has shown that AE testing can be used to monitor the tribological properties of simulated articulating joint surfaces. With machine learning and deep transfer learning techniques, models with high accuracy and high sensitivity can be built to classify the acquired AE signals based on simulated real-life artificial joint damage modes. This confirms the initial hypothesis that with AE testing, a more dynamic, highly specific and highly sensitive process of identifying and diagnosing artificial joint pathologies can be developed, thereby reducing patient discomfort and NHS expenditure

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201