IEEE Transactions on Pattern Analysis and Machine Intelligence

The task of condition monitoring and fault diagnosis of rolling element bearing is often cumbersome and labour intensive. Various techniques have been proposed for rolling bearing fault detection and diagnosis. The challenge however, is to efficiently and accurately extract features from signals acquired from these elements, particularly in the time–frequency domain. A new time–frequency technique, known as basis pursuit, was recently developed. This paper presents an application of this new basis pursuit method in the extraction of features from signals collected from faulty rolling bearings with inner race and outer race faults. Results obtained using this new technique were compared with discrete wavelet packet analysis (DWPA) and the matching pursuit technique. Basis pursuit represents features with very fine resolution and sparsity in the time–frequency domain thus rendering easier interpretation of the analysed results. The technique also improves the signal to noise ratio so that subsequent fault detection and identification can be conducted with confidence.

Fault diagnosis of rolling element bearings using basis pursuit

Novel fusion method for visible light and infrared images based on NSST–SF–PCNN

Multi-focus image fusion: A Survey of the state of the art

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

The present day camera systems have the limitation of acquiring the clearer image of a scene having objects at different distances. This limitation can be overcome by fusion of multiple images of the scene taken with different camera settings. The fusion of these images comes under the category of multifocus image fusion. In the existing method of image, fusion partitioned source image blocks are fused by pulse coupled neural network (PCNN) based on their clarity measure. PCNN plays an important role in the image fusion process in choosing the best-quality image block for fused image. Fusion method becomes tedious and time consuming because of the inherent complexity of PCNN. In this study, a modified approach of PCNN suitable for application in image fusion technique is proposed by reducing the processing time and computational complexity. The modifications proposed are in linking and feeding field of PCNN. This study presents a method for multifocus image fusion by using modified PCNN (MPCNN) with spatial frequency (SF) and energy of Laplacian (EOL) as clarity measures. The proposed method of image fusion using MPCNN results in better quality of fused image with reduced root mean square error (RMSE) and computational time requirements as compared to conventional PCNN.

Multifocus image fusion using modified pulse coupled neural network for improved image quality

Glaucoma is a class of eye disorder; it causes progressive deterioration of optic nerve fibres. Discrete wavelet transforms (DWTs) and empirical wavelet transforms (EWTs) are widely used methods in the literature for feature extraction using image decomposition. However, to increase the accuracy for measuring features of images a hybrid and concatenation approach has been presented in the proposed research work. DWT decomposes images into approximate and detail coefficients and EWT decomposes images into its sub band images. The concatenation approach employs the combination of all features obtained using DWT and EWT and their combination. Extracted features from each of DWT, EWT, DWTEWT and EWTDWT are concatenated. Concatenated features are normalised, ranked and fed to singular value decomposition to find robust features. Fourteen robust features are used by support vector machine classifier. The obtained accuracy, sensitivity and specificity are 83.57, 86.40 and 80.80%, respectively, for tenfold cross validation which outperforms the existing methods of glaucoma detection.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5297

Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images

Glaucoma is one of the leading causes of irreversible vision loss, it progresses gradually without easily noticeable symptoms. The detection of glaucoma in the early stage is crucial as it may help to decelerate the progress. The traditional instrument methods are manual, time-consuming and less accurate. Hence, the automated diagnosis of glaucoma is needed for detection of glaucoma in the early stage with high accuracy. The flexible analytic wavelet transform (FAWT) based novel method has been proposed for the classification of glaucoma stages. In the proposed method, FAWT has been used to decompose the preprocessed images into various sub-band images. Then, ReliefF and sequential box-counting (SBC) algorithms are applied to extract the various entropies and fractal dimension (FD) based features, respectively. Further, the extracted feature values are ranked using Fisher’s linear discriminant analysis (LDA) dimensionally reduction. Finally, the higher rank features have been used for the classification of glaucoma stages using least squares-support vector machine (LS-SVM) classifier. The proposed method has been evaluated on publicly available large and diverse glaucoma database. The classification accuracy of the proposed method is 93.40% using tenfold cross-validation. The proposed method has demonstrated better performance for glaucoma classification as compared to the existing methods. The proposed method is ready to help the ophthalmologist in their daily screening for glaucoma detection.

Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images

Ball bearings are considered as a critical element in various mechanical systems. Vibration signal analysis is very effective method for finding bearing fault. Accelerometers are used to capture the multi-component vibration signal generated in the machine when it is in use. Various methods based on empirical mode decomposition (EMD) have been used for ball bearing fault diagnosis. EMD method usually suffered from the boundary distortion of intrinsic mode function. Classification of ball bearing fault is one of the challenging tasks in the field of mechanical systems. Various classification schemes such as support vector machine (SVM), K-means clustering, extreme learning machine (ELM) have been used for the classification of ball bearing fault. In this study, footprint analysis of Hilbert transform along with the neural network has been done for ball bearing fault analysis. A comparative analysis of the proposed research study has been done with available methods such as SVM and ELM. A high fault classification accuracy has been achieved using the proposed method for detection of ball bearing fault.

Bearing fault classification using ANN-based Hilbert footprint analysis

Glaucoma is a critical and irreversible neurodegenerative eye disorder caused by damaging optical nerve head due to increased intra-ocular pressure within the eye. Detection of glaucoma is a critical job for ophthalmologists. This study presents a novel and more accurate method for automated glaucoma detection using quasi-bivariate variational mode decomposition (QB-VMD) from digital fundus images. In total, 505 fundus images are decomposed using QB-VMD method which gives band limited sub-band images (SBIs) centred around a particular frequency. These SBIs are smooth and free from mode mixing problems. The glaucoma detection accuracy depends on the most useful features as it captured appropriate information. Seventy features are extracted from QB-VMD SBIs. Extracted features are normalised and selected using ReliefF method. Selected features are then fed to singular value decomposition to reduce their dimensionality. Finally, the reduced features are classified using least square support vector machine classifier. The obtained glaucoma detection accuracies are 85.94 and 86.13% using three- and ten-fold cross validation, respectively. Obtained results are better than the existing. It may become a suitable method for ophthalmologists to examine eye disease more accurately using fundus images.

Dheeraj Agrawal

Papers

Multifocus image fusion using modified pulse coupled neural network for improved image quality

Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images

Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images

Bearing fault classification using ANN-based Hilbert footprint analysis

Automated glaucoma detection using quasi-bivariate variational mode decomposition from fundus images