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.
•Data driven condition monitoring is limited to cases where in-service data exists.•Simulation models are proposed as a data source for machine learning training data.•Simulation-trained classifiers ...achieve up to 94% accuracy on four exp. datasets.
Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.
For intelligent condition monitoring (ICM) tasks of machine tools (MTs), physics-based and data-driven models typically suffer from two major challenges that constraint their applicability: first, ...the complex machining parameters set up along with the incompleteness of physics-based models and second, the limited representation ability of small-scale dataset for data-driven models. Considering that it is impractical for the cases of MTs to obtain sufficient scale and well-balanced dataset due to unaffordable specimen cost and strict manufacturing schedule. Accordingly, this paper proposes a new physics-informed scaling evolutionary transformer network, abbreviated as PIS-ETN, to incorporate prior knowledge into the ICM model. Specifically, it mainly includes three parts. First, a texture digital twin (TDT) model is designed to exploit prior knowledge from machining parameters and semi-observable sensor information. Secondly, a texture knowledge embedding module is designed to enhance representation capability. Thirdly, the Pareto-optimal solution space is adopted for further architecture optimization. The experiments indicate that the designed TDT model can effectively provide rich prior empirical knowledge for the designed scaling lightweight model and accelerate model convergence. The proposed lightweight architecture with its Pareto optimal training strategy shows promising fine-grained texture representation ability.
•Presents a sensor fusion approach with the investigation of sensor signal features.•5 sensor signals were investigated to understand their capability on flank wear.•A tool condition monitoring ...system was set up to collect and evaluate data.•Acoustic emission, current, temperature, force and vibration signals were measured.•Acoustic emission and temperature signals were found as effective on flank wear.
Monitoring of the cutting area with different type of sensors requires confirmation for composing sensor fusion to obtain longer tool life and high-quality product. The complex structure of machining and interaction between variables affect the influence of parameters on quality indicators. Using multiple sensors provide comparison of information acquired from different resources and make easier to decide about tool and workpiece condition. In this experimental research for the first time, five different sensors were adopted to a lathe for collecting data to measure the capability of each sensor in reflecting the tool wear. Cutting forces, vibration, acoustic emission, temperature and current measurements were carried out during turning of AISI 5140 with coated carbide tools. Considering the graphical investigation, the successes of sensors on detection of progressive flank wear and tool breakage were investigated. Besides, the effects of cutting parameters on measured variables were interpreted considering graphs. According to results, temperature and acoustic emission signals seem to be effective about 74% for flank wear. In addition, fuzzy logic based prediction of flank wear was performed with the assistance of temperature and acoustic emission sensors with high accuracy which demonstrates their availability for sensor fusion. Tool breakage occurs instantly which can prevent with the assistance of sensor signals and tangential and feed cutting forces, acoustic emission and vibration signals seem as reliable indicators for approaching major breakage. Sensor fusion based turning provides confirmed information which enables more reliable, robust and consistent machining.
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