A novel implementation of vibration signal decomposition for estimation of degradation in rotating plant

Effective and transparent monitoring of rotating plant assets is essential to the continued reliable operation of power stations. Rotating plant monitoring generally includes analysis of vibration signals, where operations and maintenance engineers use the output from vibration sensors to justify the continued operation of the plant or plan for maintenance interventions where necessary. One common approach to such vibration monitoring is the adoption of alarm driven strategies where certain operational or mechanical interventions are performed when thresholds are triggered due to deviations from a predefined operational envelope. This reactive intervention approach, however, does not provide operators or equipment manufacturers with any insight into the long-term degradation of a rotating plant item, which could be used to mitigate unplanned stoppages. This paper proposes the novel implementation of Empirical Mode Decomposition to boiler feed pump vibration signals, alongside subsequent statistical analysis of the decomposed signals to estimate time-frames associated with alarm violations and entry into predefined zones of operation. Such a technique provides pump operators with information that can be used to plan for future maintenance interventions and pump manufactures with insight into the likely degradation of their product during sustained operation

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

Abstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings