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Rotating machines is an essential part of any manufacturing industry. The sudden breakdown of such machines due to improper maintenance can also lead to the industries' shutdown. The era of the 4th industrial revolution is taking its major shape concerning maintenance strategies, notable being in predictive maintenance. Fault prediction and diagnosis is the major concern in predictive maintenance as this is the major issue faced by all the maintenance engineers. Most of the bibliometric literature review studies that are accessible focus on fault diagnosis in rotating machines, mainly focusing on a single type of fault. However, there isn't a thorough analysis of the literature that focuses on the \"multi-fault diagnosis using multi-sensor data\" aspect of rotating machines. In this regard, this paper reviews the literature on the “multi-Fault diagnosis using multi-sensor data fusion” of Industrial Rotating Machines employing Machine learning/Deep learning techniques. A hybrid bibliometric approach was used to analyze articles from the “Web of Science” and “Scopus” Database for the last 10 years. The method for literature analysis used, is quantitative as well as qualitative, as not only the traditional approach (bibliometric and network analysis) but also a novel method named ProKnow-C is used, and it entails a number of phases, that includes intelligent and extensive filtering from the large set of results and finally selecting the articles that are more pertinent to the research theme. Based on available publications, an analysis is performed on year-by-year publication data, article types, linguistic distribution of articles, funding sponsors, affiliations, citation analysis and the relationship between keywords, authors, etc. to provide an in-depth vision of research trends in the related area. The paper also focuses on the maintenance strategies, predictive maintenance approaches, AI algorithms, Multi sensor data fusion, challenges, and future directions in “multi-fault diagnosis using multi-sensor data fusion” in rotating machines. The foundational work done in the field, the most prolific papers and the key research themes within the research area are all identified in this bibliometric survey.

Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry.

A weak character signal with low frequency can be detected based on the mechanism of vibrational resonance (VR). The detection performance of VR is determined by the synergy of a weak low-frequency input signal, an injected high-frequency sinusoidal interference and the nonlinear system(s). In engineering applications, there are many weak fault signals with high character frequencies. These fault signals are usually submerged in strong background noise. To detect these weak signals, an adaptive detection method for a weak high-frequency fault signal is proposed in this paper. This method is based on the mechanics of VR and cascaded varying stable-state nonlinear systems (VSSNSs). Partial background noise with high frequency is regarded as a special type of high-frequency interference and an energy source that protrudes a weak fault signal. In this way, high-frequency background noise is utilized in a VSSNS. To improve the detection ability, manually generated high-frequency interference is injected into another VSSNS. The VSSNS can be transformed into a monostable state, bistable state or tristable state by tuning the system parameters. The proposed method is validated by a simulation signal and industrial applications. The results show the effectiveness of the proposed method to detect a weak high-frequency character signal in engineering problems.

In recent years, deep learning models have increasingly been employed for fault diagnosis in rotating machines, with remarkable results. However, the accuracy and reliability of these models in fault diagnosis tasks can be significantly influenced by critical input parameters, such as the sample size, the number of data points within each sample, and the augmentation stride in vibration signal analysis. To address this challenge, this paper proposes a new adaptive method based on Bayesian optimization to determine the optimal combination of these input parameters from raw vibration signals and enhance the diagnostic performance of deep learning models. This study utilizes a one‐dimensional convolutional neural network (1‐D CNN) as the deep learning model for fault classification. The proposed adaptive 1‐D CNN‐based fault diagnosis method is validated via vibration signals collected from motor rolling bearings and achieves a fault diagnosis accuracy of 100%. Compared with existing CNN‐based diagnosis methods, this adaptive approach not only achieves the highest accuracy on the testing set but also demonstrates stable performance during training, even under varying operating conditions. These results indicate the importance of optimizing the input parameters of deep learning models employed in fault diagnosis tasks.

Noise cancellation is one of the most successful applications of the wavelet transform. Its basic idea is to compare wavelet decomposition coefficients with the given thresholds and only keep those bigger ones and set those smaller ones to zero and then do wavelet reconstruction with those new coefficients. It is most likely for this method to treat some useful weak components as noise and eliminate them. Based on the cyclostationary property of vibration signals of rotating machines, a novel wavelet noise cancellation method is proposed. A numerical signal and an experimental signal of rubbing fault are used to test and compare the performances of the new method and the conventional wavelet based denoising method provided by MATLAB. The results show that the new noise cancellation method can efficaciously suppress the noise component at all frequency bands and has better denoising performance than the conventional one.

On any website or in encyclopaedias such as Britannica or Wikipedia, under the entry ‘heuristics,’ one can find numerous definitions, references, and examples from various areas of life. However, the authors of this article have been unable to find examples relevant to technology, particularly in mechanical engineering. This fact inspired us to address this topic, especially since many concrete examples from practice and everyday life seem well-suited to demonstrating the relevance of heuristic methodologies in technical sciences. According to the authors, turbomachinery appears to be of particular interest in this context. This is critical machinery, i.e., machinery whose failure threatens human life. Hence the importance of developing advanced tools to analyze them, especially across the entire operating range (both stable and unstable). With these tools, one can effectively use their intellect, intuition, and common sense in the decision-making process. A classic heuristic symbiosis is thus formed. The paper demonstrates an advanced computer system called MESWIR, developed at the Institute of Fluid-Flow Machinery of the Polish Academy of Sciences in Gdańsk (IMP PAN), which generates a range of interesting diagnostic information, including multiple whirls and stochastic errors related to the unbalance vector. The research was conducted using high-speed, low-power turbines as examples. Although there is no formal theoretical proof of their correctness, the results obtained facilitate drawing the right conclusions and making informed decisions, which is the essence of decision-making heuristics.

This study examines the impact of the mother wavelet, sensor selection, and machine learning (ML) models for smart fault diagnosis of rotating machines via discrete wavelet transform (DWT). The ability of Daubechies, Haar, Biorthogonal (Bior), Symlets (Sym), and Coiflets (Coif) wavelets is measured in terms of distinguishing imbalance, horizontal/vertical misalignment, and overhang/underhang bearing ball, cage, and outer race faults. For this purpose, single-step and two-step fault monitoring (SSFM and TSFM) approaches are proposed. In SSFM, the ML models detect the fault type by the healthy and faulty signals. In TSFM, the built models first determined whether the machine is faulty or not. If it is, then the models detect the fault type. As ML models, Random Forest (RF), AdaBoost with C4.5 (AB-C4.5), and two artificial neural network algorithms are trained by the features of DWT. Besides, the effect of the sensor type on the fault diagnosis is measured by considering the tachometer, microphone, and two accelerometers individually and combined. The results are interpreted regarding the evaluation metrics such as accuracy, precision, recall, confusion matrix, and model built time. It is concluded that Bior3.1 and Haar wavelets distinguish the fault type more accurately than other wavelets. Besides, the RF-Bior3.1 give the best results for SSFM and TSFM by accuracy values of 99.80% and 99.98%, respectively. It is also found that the sensor type is correlated with the selected mother wavelet.

Bearings are the backbone of industrial machines that can shut down or damage the whole process when a fault occurs in them. Therefore, health diagnosis and fault identification in the bearings are essential to avoid a sudden shutdown. Vibration signals from the rotating bearings are extensively used to diagnose the health of industrial machines as well as to analyze their symmetrical behavior. When a fault occurs in the bearings, deviations from their symmetrical behavior can be indicative of potential faults. However, fault identification is challenging when (1) the vibration signals are recorded from variable speeds compared to the constant speed and (2) the vibration signals have diverse fault depths. In this work, we have proposed a highly accurate Deep Convolution Neural Network (DCNN)–Long Short-Term Memory (LSTM) model with a SoftMax classifier. The proposed model offers an innovative approach to fault diagnosis, as it obviates the need for preprocessing and digital signal processing techniques for feature computation. It demonstrates remarkable efficiency in accurately diagnosing fault conditions across variable speed vibration datasets encompassing diverse fault conditions, including but not limited to outer race fault, inner race fault, ball fault, and mixed faults, as well as constant speed datasets with varying fault depths. The proposed method can extract the features automatically from these vibration signals and, hence, are excellent to enhance the performance and efficiency to diagnose the machine’s health. For the experimental study, two different datasets—the constant speed with different fault depths and variable speed rotating machines—are considered to validate the performance of the proposed method. The accuracy achieved for the variable speed rotating machine dataset is 99.40%, while for the diverse fault dataset, the accuracy reaches 99.87%. Furthermore, the experimental results of the proposed method are compared with the existing methods in the literature as well as the artificial neural network (ANN) model.

Composite materials present several applications in different types of mechanical components and structures due to their wide range of advantages, such as high stiffness and resistance as compared to their weight and their ability to change stiffness and damping characteristics through the manipulation of their properties. In this way, researchers in the field of rotor dynamics have seen the use of these materials instead of metallic ones as an opportunity to maximize operating speeds, reduce the time of acceleration and deceleration, increase structural efficiency, among other aspects. In the present contribution, a finite element model of a composite material hollow shaft was formulated by considering the Kelvin–Voigt rheological model and the simplified homogenized beam theory to obtain the properties of the composite material shaft, from which internal damping and stiffness matrix are determined. The dynamic behavior of the system is influenced by internal damping and stiffness matrix. Therefore, interval uncertainty and sensitivity analyses were applied to a composite material shaft under two different sets of boundary conditions, namely free–free condition and the shaft assembled as a component of an experimental rotor-bearing system. The main scientific contribution of this work lies in the comprehensive evaluation of the composite material hollow shaft’s frequency response functions by considering a more extensive set of uncertain parameters as compared to previous studies. Moreover, the present work introduces a dedicated analysis of the effects of rotation speed on the shaft’s dynamic behavior, which was not addressed in earlier contributions. The obtained results demonstrated that the most important parameters for changing the vibration responses of the shaft depend on the excitation frequency (rotor at rest) and rotation speed (rotating machine).

Many industrial rotating machines driven by asynchronous motors are often affected by detrimental torsional vibrations. In this paper, a method of attenuation of torsional vibrations in such objects is proposed. Here, an asynchronous motor under proper control can simultaneously operate as a source of drive and actuator. Namely, by means of the proper control of motor operation, it is possible to suppress torsional vibrations in the object under study. Using this approach, both transient and steady-state torsional vibrations of the rotating machine drive system can be effectively attenuated, and its precise operational motions can be assured. The theoretical investigations are conducted by means of a structural mechanical model of the drive system and an advanced circuit model of the asynchronous motor controlled using two methods: the direct torque control – space vector modulation (DTC-SVM) and the rotational velocity-controlled torque (RVCT) based on the momentary rotational velocity of the driven machine working tool. From the obtained results it follows that by means of the RVCT technique steady-state torsional vibrations induced harmonically and transient torsional vibrations excited by switching various types of control on and off can be suppressed as effectively as using the advanced vector method DTC-SVM.