Sukhjeet Singh

Bio: Sukhjeet Singh is an academic researcher from Guru Nanak Dev University. The author has contributed to research in topics: Bearing (mechanical) & Fault (power engineering). The author has an hindex of 9, co-authored 32 publications receiving 395 citations. Previous affiliations of Sukhjeet Singh include Post Graduate Institute of Medical Education and Research & Indian Institute of Technology Ropar.

Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring

Abstract: Induction motors have been responsible for running mechanical systems in the industry for many decades. Their diagnosis still remains a hot quest for the researchers using various techniques. In this study, motor current signature analysis (MCSA) technique has been used to detect the faulty bearing installed in load machine (coupled to an induction motor). It has been seen that faulty bearings installed in load machines do not directly alter airgap eccentricity of an induction motor. In fact, these bearing faults affect the resultant torque of an induction motor. As modulating fault components show very low amplitude, these are usually masked by noise. This paper is devoted towards extracting features of faulty components efficiently from stator current using continuous wavelet transform. This methodology is assessed for detecting outer race faults in bearings installed in load machines using MCSA.

Treatment of liver abscess: prospective randomized comparison of catheter drainage and needle aspiration.

Abstract: Background The aim of the study was to evaluate the clinical presentation, and to investigate the effectiveness of continuous catheter drainage in comparison to needle aspiration in the treatment of liver abscesses. Methods This is a prospective randomized comparative study of 60 patients, presented in outpatient and emergency department at the hospital, randomized equally into two groups, percutaneous needle aspiration and pigtail catheter drainage. The effectiveness of either treatment was measured in terms of duration of hospital stay, days to achieve clinical improvement, 50% reduction in abscess cavity size and total/near total resolution of abscess cavity. Independent t- test was used to analyze these parameters. Results The success rate was significantly better in catheter drainage group (P=0.006). The patients in pigtail catheter drainage group showed earlier clinical improvement (P=0.039) and 50% decrease in abscess cavity volume (P=0.000) as compared to those who underwent percutaneous needle aspiration. Conclusion Percutaneous catheter drainage is a better modality as compared to percutaneous needle aspiration especially in larger abscesses which are partially liquefied or with thick pus.

Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients

Abstract: Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Abstract: Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Abstract: Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.