Artificial Intelligence Enhanced Reliability Assessment Methodology With Small Samples

JITL-MBN: A Real-Time Causality Representation Learning for Sensor Fault Diagnosis of Traction Drive System in High-Speed Trains

A traction drive system (TDS) in high-speed trains is composed of various modules including rectifier, intermediate dc link, inverter, and others; the sensor fault of one module will lead to abnormal measurement of sensor in other modules. At the same time, the fault diagnosis methods based on single-operating condition are unsuitable to the TDS under multi-operating conditions, because a fault appears various in different conditions. To this end, a real-time causality representation learning based on just-in-time learning (JITL) and modular Bayesian network (MBN) is proposed to diagnose its sensor faults. In specific, the proposed method tracks the change of operating conditions and learns potential features in real time by JITL. Then, the MBN learns causality representation between faults and features to diagnose sensor faults. Due to the reduction of the nodes number, the MBN alleviates the problem of slow real-time modeling speed. To verity the effectiveness of the proposed method, experiments are carried out. The results show that the proposed method has the best performance than several traditional methods in the term of fault diagnosis accuracy.

Rolling mill health states diagnosing method based on multi-sensor information fusion and improved DBNs under limited datasets

Due to the harsh working conditions and high cost of data acquisition in the actual environment of modern rolling mills, the resulting limited datasets issue leading in performance collapse of traditional deep learning (DL) methods has been plaguing researchers and needs to be urgently addressed. Hence, an improved single-sensor Deep Belief Network (IDBN) is first proposed to repetitively extract valuable information from hidden features and visible features of the previous improved Restricted Boltzmann Machine (IRBM) to alleviate this issue. Next, the multi-sensor IDBNs (MSIDBNs) are applied to obtain complementary and enriched health state features from different multi-sensor data to cope with limited datasets more effectively. Then, the Fast Fourier Transform (FFT) technique is adopted for the multi-sensor information to further enhance the effectiveness of feature extraction. Most importantly, the redefined pretraining and finetuning stages are designed for the MSIDBNs. Meanwhile, the optimal placement of multiple sensors is fully discussed to obtain the most efficient information about health content. Finally, two limited datasets are conducted to validate the superiority of the proposed MSIDBNs. Results show that the proposed MSIDBNs are capable of extracting valuable features from multi-sensor information and achieving more remarkable performance compared with the state-of-the-art (SOTA) methods under limited datasets.

Application of Generation Adequacy Analysis for Reliability Evaluation of a Floating Production Storage and Offloading Platform Power System

This paper proposes the application of generation adequacy analysis for reliability evaluation of an insulated power generation system that supplies a FPSO (Floating Production Storage and Offloading) oil and gas production platform. The frequency and duration method was adopted for generating system reliability evaluation. The historical reliability data of the floating production storage and offloading platform power system and the continuous Markov process are used to determine the generator’s reliability model. The load model was also based on the platform daily peak load variation curve. The system risk indexes were obtained using Monte Carlo simulation. Two system scenarios were simulated using different failure data for one of the generators and the software PowerFactory© has been used as a tool for this simulation. For a complete generation system modeling, frequency and duration methods were developed to calculate the probabilities, frequencies and duration of the system states. Numerical results are presented and discussed to demonstrate the applicability of the proposed method.