IEEE Galveston Bay Section

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for commercial air-conditioning (HVAC/ACMV) systems detect some of the critical faults only at high severity levels resulting in higher operation and maintenance costs. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. Our aim is to develop a unique, single hybrid scheme involving both feature extraction and classification using electrical signals-based holistic HMS for various types of critical faults of an HVAC/ACMV and its associated component. The developed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals in a non-intrusive way. Speaker(s): Dr. Hasmat Malik, Virtual: https://events.vtools.ieee.org/m/323107