Health Monitoring: Developed and deployed algorithms to detect drifts in sensor data from industrial machines, resulting in a 75% reduction in false positive alarms.
·ML Experiment Pipeline: Designed and implemented a data processing pipeline in Kedro, encompassing data ingestion, cleaning, and machine learning model integration for fault detection and pattern recognition.
Few-shot Learning: Designed and deployed a Few-shot (One-shot and Five-shot) learning model to classify various faults (Healthy, Unbalance, Misalignment, Inner Race, Outer Race and Ball Bearing) in industrial machines using limited acceleration data.
GAN-based Image Generation: Successfully generated STFT-based spectrogram images using GANs and performed fault analysis with sophisticated 2D CNN models.
Fault Classification: Designed a customized 2-D CNN model and a 1-D CNN +LSTM model to classify multiple faults (Healthy, Unbalance, Misalignment, Inner Race, Outer Race and Ball Bearing) in industrial machines, enhancing overall system diagnostics accuracy by 90%.
Transfer Learning: Leveraged transfer learning techniques (AlexNet, VGG-16, Inception, and ResNet) to identify multiple faults in industrial machinery using time-frequency distribution-based images.
Signal-to Image Transformation: Implemented advanced time-frequency distribution techniques (Spectrogram, Continuous Wavelet Transform Wavelet-based Synchrosqueezing Transform, Fourier-based Synchrosqueezing Transform, Wigner-Ville Distribution, Constant-Q Nonstationary Gabor Transform, Hilbert Huang Transform, Scattergram) to generate images for training deep learning models on machine fault classification.
Harmonic Series Detection: Analyzed the harmonic series of fundamental and fault frequencies (BPFO, BPFI, BSF, and GMF) using Harmonic Product Spectrum, Kurtogram, and Cepstrum analysis.
ML Models Monitoring: Employed NannyML to monitor ML models, focusing on performance estimation and change detection, which enhanced machine lifespan prediction accuracy by 20%.