Rolling element bearings health status indictor analysis

According to the vibration mechanism of ship gas turbine rolling element bearings common failure modes, the variation of the common indicators during the rolling element bearings health status degradation process is analyzed, and the reflection ability of the various indicators is studied based on the consistency and sensitivity. The results show that the Root-Mean-Square value, Peak-Peak value, Wavelet Energy Spectrum Entropy and Singular Spectrum Entropy can effectively reflect the health state change of rolling element bearings

Reliability Analysis of Complex Systems with Failure Propagation

Failure propagation is a critical factor for the reliability and safety of complex systems. To recognise and identify failure propagation of systems, a deep fusion model based on deep belief network (DBN) and Bayesian structural equation model (BSEM) is proposed. The deep belief network is applied to extract features between status monitoring data and the performance degradation in different failure components. To calculate the path weight of failure propagation, the Bayesian structural equation model is proposed to study the relationship among different fault modes. After getting the performance degradation of each fault through DBN and calculating the path weight of fault propagation by BSEM, it is available to get the overall reliability of the system. The aircraft landing gear system with 19 fault patterns is selected to evaluate the feasibility of the proposed deep fusion model. The results demonstrate that the overall reliability of the system can be obtained by analysing the fault propagation of multiple fault patterns, and the proposed model has a lower deviation than traditional back propagation neural network

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a CNN architecture has rarely been studied. In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN. This enables the first CWConv layer to discover more meaningful filters. Furthermore, only the scale parameter and translation parameter are directly learned from raw data at this CWConv layer. This provides a very effective way to obtain a customized filter bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental verification using data from laboratory environment are carried out to verify effectiveness of the proposed method for mechanical fault diagnosis. The results show the importance of the designed CWConv layer and the output of CWConv layer is interpretable. Besides, it is found that WKN has fewer parameters, higher fault classification accuracy and faster convergence speed than standard CNN

Recurrent neural networks and its variants in Remaining Useful Life prediction

Data-driven techniques, especially artificial intelligence (AI) based deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the rapid growth of the industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper reviews and compares the state-of-the-art DL approaches for RUL prediction focusing on Recurrent Neural Networks (RNN) and its variants. It has been observed from the results for a publicly available dataset that Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks outperform the basic RNNs, and the number of the network layers affects the performance of the prediction

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

Advanced data-driven methods for prognostics and life extension of assets using condition monitoring and sensor data.

A considerable number of engineering assets are fast reaching and operating beyond their orignal design lives. This is the case across various industrial sectors, including oil and gas, wind energy, nuclear energy, etc. Another interesting evolution is the on-going advancement in cyber-physical systems (CPS), where assets within an industrial plant are now interconnected. Consequently, conventional ways of progressing engineering assets beyond their original design lives would need to change. This is the fundamental research gap that this PhD sets out to address. Due to the complexity of CPS assets, modelling their failure cannot be simplistically or analytically achieved as was the case with older assets. This research is a completely novel attempt at using advanced analytics techniques to address the core aspects of asset life extension (LE). The obvious challenge in a system with several pieces of disparate equipment under condition monitoring is how to identify those that need attention and prioritise them. To address this gap, a technique which combined machine learning algorithms and practices from reliability-centered maintenance was developed, along with the use of a novel health condition index called the potential failure interval factor (PFIF). The PFIF was shown to be a good indicator of asset health states, thus enabling the categorisation of equipment as “healthy”, “good ” or “soon-to-fail”. LE strategies were then devoted to the vulnerable group labelled “good – monitor” and “soon-to-fail”. Furthermore, a class of artificial intelligence (AI) algorithms known as Bayesian Neural Networks (BNNs) were used in predicting the remaining useful life (RUL) for the vulnerable assets. The novelty in this was the implicit modelling of the aleatoric and epistemic uncertainties in the RUL prediction, thus yielding interpretable predictions that were useful for LE decision-making. An advanced analytics approach to LE decision-making was then proposed, with the novelty of implementing LE as an on-going series of activities, similar to operation and maintenance (O&M). LE strategies would therefore be implemented at the system, sub-system or component level, meshing seamlessly with O&M, albeit with the clear goal of extending the useful life of the overall asset. The research findings buttress the need for a paradigm shift, from conventional ways of implementing LE in the form of a project at the end of design life, to a more systematic approach based on advanced analytics.Shafiee, Mahmood (Associate)PhD in Energy and Powe