An intelligent compound gear-bearing fault identification approach using Bessel kernel-based time-frequency distribution

The most crucial transmission components utilized in rotating machinery are gears and bearings. In a gearbox, the bearings support the force acting on the gears. Compound Faults in both the gears and bearings may cause heavy vibration and lead to early failure of components. Despite their importance, these compound faults are rarely studied since the vibration signals of the compound fault system are strongly dominated by noise. This work proposes an intelligent approach to fault identification of a compound gear-bearing system using a novel Bessel kernel-based Time-Frequency Distribution (TFD) called the Bessel transform. The Time-frequency images extracted using the Bessel transform are used as an input to the Convolutional Neural Network (CNN), which classifies the faults. The effectiveness of the proposed approach is validated with a case study, and a testing efficiency of 94% is achieved. Further, the proposed method is compared with the other TFDs and found to be effective

A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions.

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions

A global survey on the current state of practice in Zero Defect Manufacturing and its impact on production performance

To be competitive in dynamic and global markets, manufacturing companies are continuously seeking to apply innovative production strategies and methods combined with advanced digital technologies to improve their flexibility, productivity, quality, environmental impact, and cost performance. Zero Defect Manufacturing is a disruptive concept providing production strategies and methods with underlying advanced digital technologies to fill the gap. While scientific knowledge within this area has increased exponentially, the current practices and impact of Zero Defect Manufacturing on companies over time are still unknown. Therefore, this survey aims to map the current state of practice in Zero Defect Manufacturing and identify its impact on production performance. The results show that although Zero Defect Manufacturing strategies and methods are widely applied and can have a strong positive impact on production performance, this has not always been the case. The findings also indicate that digital technologies are increasingly used, however, the potential of artificial intelligence and extended reality is still less exploited. We contribute to theory by detailing the research needs of Zero Defect Manufacturing from the practitioner’s perspective and suggesting actions to enhance Zero Defect Manufacturing strategies and methods. Further, we provide practical and managerial suggestions to improve production performances and move towards sustainable development and zero waste.publishedVersio

Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning.

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets

Effect of excitation frequency on nonlinear vibration of crack fault in multi-stage gear transmission system

In a multi-stage gear transmission system, the motion state of the system will change with the excitation frequency, and the frequency characteristics will also change accordingly. If this change is not taken into account, there is often a great deviation in identifying and judging system faults according to unified standards, especially when the system has such early undetectable fault as crack. In this paper, the dimensionless differential equations of motion of multistage gear transmission system are established. The stiffness model of gear tooth crack is established by potential energy method. The changes of the motion state of the system with the increase of excitation frequency are obtained by calculating the displacement bifurcation diagram of cracked gear tooth. The influences of crack fault on each motion state are studied by using time domain, frequency domain, phase diagram and Poincaré cross section, and the fault frequency characteristics are summarized. By comparing the theoretical and experimental data of the vibration response characteristics of the system, the motion state of the system can be effectively determined and the crack fault can be identified

Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Machine learning techniques have been successfully applied in intelligent fault diagnosis of rolling bearings in recent years. However, in the real world industrial application, the dissimilarity of data due to changes in the working conditions and data acquisition environment often cause a poor performance of the existing fault diagnosis methods. Consequently, to address these inadequacies, this paper developed a novel method by integrating the Convolutional Neural Networks (CNNs) with the Variational Mode Decomposition (VMD) algorithms. Named as “Variational Mode Decomposition with Deep Convolutional Neural Networks (VMD-DCNNs)”, the method, in an end-to-end way, directly processes raw vibration signals without artificial experiences and manual intervention to realize the fault diagnosis of rolling bearings. In addition, the CNN technique is used to extract features from each Intrinsic Mode Function (IMF) in order to address the deficiency in extracting features from a single source and to achieve an effective and efficient fault diagnosis of rolling bearings under different environments and states. The value of parameter K of the VMD-DCNNs model is optimized by considering time complexity and generalization ability of the model. Lastly, bearing experiments are conducted to verify the superiority of the VMD-DCNNs in diagnosing fault under different conditions. The visualizations of the signals in the convolutional layer explain the reasonability in selecting the value of parameter K and they also indicate that the translational invariances in a raw IMF component have been learned by the VMD-DCNNs model

A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types’ fault detection accuracy—reached to 99.12%, 99.33% and 98.46% by the improved Dempster–Shafer evidence theory (IDS) to fuse the sensors’ results—is respectively 0.38%, 2.06% and 0.76% higher than the traditional D–S evidence theory. That indicated the effectiveness of improving the D–S evidence theory by evidence weight calculation of PCC

Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic