This paper proposes the concept of how machinery condition monitoring can be taken to the next level, through micro-sensing of tribological phenomena occurring between contacting surfaces. By ...considering wear transitions and wear rates it is possible to distinguish between benign and potentially harmful wear scenarios. By measuring the tribological phenomena associated with these conditions, it should then be possible to determine with greater accuracy the health of a machine at any point in its life. For this approach to succeed, it is necessary to develop a comprehensive and holistic monitoring strategy and target sensing technologies for the key wear factors. The paper has two main sections. Firstly, tribological phenomena and the onset of wear which sets out why and what needs to be monitored. The factors influencing the wear process are grouped into three key areas: lubricant condition, tribo-pair condition and operating condition. Through a critical and comprehensive review of developing and state-of the-art tribo-sensing, the second section identifies the potential technologies for monitoring or measuring the physical parameters within these three groupings and thus sets out how the next generation of machine condition monitoring will need to evolve in order to achieve early wear detection and the related benefits.
•The factors influencing wear are grouped into three areas: lubricant condition, tribo-pair condition and operating condition.•In-situ measurements of tribological phenomena provide an early indication of wear, ahead of impending failure.•We use the derivative of instantaneous wear rate (dIWR) to distinguish between natural and induced wear transitions.•Wear transitions and rates distinguish benign and potentially harmful scenarios to determine the health of a machine.•Reviews monitoring tribo-phenomena, identifies existing capability and gaps and how current technologies address these gaps.
Wind turbines are, generally, placed at remote locations and are subject to harsh environmental conditions throughout their lifetimes. Consequently, major failures in wind turbines are expensive to ...repair and cause losses of revenue due to long down times. Asset management using optimal maintenance strategies can aid in improving the reliability and the availability of wind turbines, thereby making them more competitive. Various mathematical optimization models for maintenance scheduling have been developed for application with wind turbines. Typically, these models provide either an age based or a condition based preventive maintenance schedule. This paper proposes a wind turbine maintenance management framework which utilizes operation and maintenance data from different sources to combine the benefits of age based and condition based maintenance scheduling. A mathematical model called Preventive Maintenance Scheduling Problem with Interval Costs (PMSPIC) is presented with modification for the maintenance optimization considering both age based and condition based failure rate models. The application of the maintenance management framework is demonstrated with case studies which illustrate the advantage of the proposed approach.
•A framework which provides tools for utilization of data from various sources for optimal maintenance strategy is presented.•An optimization model is presented and compared to a frequently used simple model and the implications are discussed.•The optimization model is modified for a hybrid maintenance plan, considering age-based and condition-based strategies.•A novel approach for utilizing the proportional hazards model is presented for condition based maintenance scheduling.
Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. However, while considerable research has been conducted in industrial and academic ...settings, the complexity of milling processes continues to complicate the implementation of TCM. This paper presents a review of the state-of-the-art methods employed for conducting TCM in milling processes. The review includes three key components: (1) sensors, (2) feature extraction, and (3) monitoring models for the categorization of cutting tool states in the decision-making process. In addition, the primary strengths and weaknesses of current practices are presented for these three components. Finally, this paper concludes with a list of recommendations for future research.
Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery ...is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.
Due to the demands of Computer-Integrated Manufacturing (CIM), the Tool Condition Monitoring (TCM) system, as a major component of CIM, is essential to improve the production quality, optimize the ...labor and maintenance costs, and minimize the manufacturing loses with the increase in productivity. To look for a reliable, efficient, and cost-effective solution, various monitoring systems employing different types of sensing techniques have been developed to detect the tool conditions as well as to monitor the abnormal cutting states. This paper explores the use of audible sound signals as sensing approach to detect the cutting tool wear and failure during end milling operation by using the Support Vector Machine (SVM) learning model as a decision-making algorithm. In this study, sound signals collected during the machining process are analyzed through frequency domain to extract signal features that correlate actual cutting phenomenon. The SVM method seeks to provide a linguistic model for tool wear estimation from the knowledge embedded in this machine learning approach. The performance evaluation results of the proposed algorithm have shown accurate predictions in detecting tool wear under various cutting conditions with rapid response rate, which provides the good solution for in-process TCM. In addition, the proposed monitoring system trained with sufficient signals collected from different positions has been proved to be position independent to monitor the tool wear conditions.
Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical ...role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.
This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that ...represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
•Theoretical investigations on correlation dimension (CD) and approximate entropy (AE) are conducted.•CD and AE have a “bilateral reduction” effect.•Kurtosis and negative entropy have a “unilateral ...reduction” effect.•CD with any dimension and AE with smaller dimension become smaller when a signal is getting sparser or more deterministic.
The sparsity of signals is of great concern in various research domains. In mechanical systems and signal processing, repetitive transients are the symptoms of localized gear and bearing faults and they are sparse signals. During the recent years, sparsity measures, such as kurtosis and Shannon entropy, have been thoroughly studied to quantify repetitive transients for machine condition monitoring. Spectral kurtosis and spectral negative Shannon entropy are two typical examples of sparsity measures for machine condition monitoring. Besides sparsity measures, complexity measures including correlation dimension (CD) and approximate entropy (AE) have been experimentally studied during the recent years. However, theoretical investigations on these two complexity measures for machine condition monitoring are seldom reported. This paper aims to fill this research gap and propose some new theorems and proofs to show that CD and AE have a “bilateral reduction” effect, which is a proper measure of entropy. Specifically, CD with any dimension and AE with smaller dimension become smaller when a signal is getting sparser or more deterministic, which is significantly different from sparsity measures that are monotonically increasing when a signal is getting from more deterministic to sparser. This new discovery is able to help readers fully understand the main difference between sparsity measures and complexity measures. In view of this discovery, it is suggested that the concept of blind fault component separation should be used to separate low-frequency periodic components (a deterministic signal) from high-frequency repetitive transients (a sparse signal) before complexity measures are used for machine condition monitoring. This suggestion aims to avoid the uncertainty of machine condition monitoring caused by low-frequency periodic components and high-frequency repetitive transients.
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•The counterweight acts as the friction pendulum to form the all-in-one prototype.•The power density can reach 1982 W m−3.•The prototype can drive the daily used electric appliance ...and wireless sensor.•The prototype can serve as a speed sensor to detect the motion state of vehicles.•The prototype supports self-powered and real-time bogie frames monitoring.
To maintain desirable service quality and operational safety, a wireless monitoring unit integrated with the vibration energy harvesting technology becomes an available choice to achieve self-powered, maintenance-free, and real-time monitoring of the train. However, owing to the bulky size and split design, to collect the mechanical energy from the bogie frame movement is still a considerable challenge for conventional harvesters. Here, we proposed a compact all-in-one on-rotor electromagnetic energy harvester. The key novelty is that a counterweight acts as the friction pendulum to produce the desired relative motion between the coils and magnet and make the device more easily install on the wheelset. Besides, the layout of the magnetic materials and coils is optimized to improve the conversion efficiency. The output performance under broad train speeds of 420–820 rpm is systematically studied to verify the improvements of the power density (up to 1982 W m−3), and the converted electricity successfully powers the daily electric appliance and the commercial wireless Bluetooth sensors. Additionally, the harvester serves as a speed sensor to detect the motion state of the vehicle. This work makes significant progress towards potential applications in the embedded self-powered wireless condition monitoring units.
•A new tool wear condition detection method based on deep learning with multi- cutting force time series signal is proposed under small samples.•Each cutting force sensor signal is expanded and ...encoded into a two- dimensional gray recurrence plot (RP), and then aggregated into a color RP.•A multi-scale edge-labeling graph neural network is proposed to extract features from aggregated color RP to establishing a fully connected graph, in which the values of edge labels are obtained by updating the nodes and edge features.•The proposed method outperforms three state-of-the-art methods with time series signal under small samples.
Tool wear condition monitoring (TCM) is an important part of machining automation. In recent years, deep learning (DL) based TCM methods have been widely researched. However, almost DL-based methods need sufficient learning samples to obtain good accuracy, which is hard for TCM in terms of cost and time. In order to enhance the recognition accuracy of DL-based TCM under small samples, this paper proposed a new improved multi- scale edge-labeling graph neural network (MEGNN). Each channel signal of a cutting force sensor is expanded to multi- dimensional data through phase space reconstruction. Then, these multi- dimensional data are encoded into a gray recurrence plot (RP), and aggregated into a color RP, which is input to MEGNN to extract features for establishing a fully connected graph. Finally, the tool wear condition is estimated through the updated edge labels using a weighted voting method. Applications of the proposed MEGNN- based method to PHM 2010 milling TCM dataset and our experiments demonstrate it outperforms three DL-based methods (CNN, AlexNet, ResNet) under small samples.
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