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At present, one of the key equipment in pillar industries is a large rotating machinery. Conducting regular health monitoring is important for ensuring safe operation of the large rotating machinery. Because vibrations sensors play an important role in the workings of the rotating machinery, measuring its vibration signal is an important task in health monitoring. This paper aims to present these.

In this work, the contact vibration sensor and the non-contact vibration sensor have been discussed. These sensors consist of two types: the electric vibration sensor and the optical fiber vibration sensor. Their applications in the large rotating machinery for the purpose of health monitoring are summarized, and their advantages and disadvantages are also presented.

Compared with the electric vibration sensor, the optical fiber vibration sensor of large rotating machinery has unique advantages in health monitoring, such as provision of immunity against electromagnetic interference, requirement of less insulation and provision of long-distance signal transmission.

Both contact vibration sensor and non-contact vibration sensor have been discussed. Among them, the electric vibration sensor and the optical fiber vibration sensor are compared. Future research direction of the vibration sensors is presented.

The paper aims to illustrate a numerical technique to calculate fields and inductances of rotating electrical machines.

The technique is based on an integral formulation of the nonlinear magnetostatic model in terms of the unknown magnetization. The solution is obtained by means of a Picard-Banach iteration whose convergence can be theoretically proved.

The proposed method has been used to build a model of a large turbine generator. In particular, the influence of end effects on flux linkages has been computed. It has been demonstrated that the 2D solution underestimates the flux linkages as well as the no load voltage of 2 per cent, while the leakage fluxes are computed by the 2D solution with errors as high as 20 per cent.

The method is advantageous in comparison to standard methods.

To investigate the feasibility of using single/multi variable optimisation techniques with vibration measurements in solving the inverse crack identification problem.

The finite element method is used to solve the forward crack problem with a special nodal crack force approach. The multi‐variable optimisation approach is reduced to a much more efficient single‐variable one by decoupling the physical variables in the problem.

It is shown that, for the crack identification problem, global optimisation algorithms perform much better than other algorithms relying heavily on objective function gradients. Simultaneous identification of crack size and location proved to be difficult. Decoupling of the physical variable is introduced and proved to provide efficient results with single‐variable optimisation algorithms.

Need for improving the reliability and accuracy of the procedure for smaller crack sizes. Need for developing and investigation more rigorous and robust multi‐variable optimisation algorithm.

Any information about approximate crack size and location provides significant aid in the maintenance and online monitoring of rotating equipment.

The paper offers practical approach and procedure for online monitoring and crack identification of slow rotating equipment.

High power rotating machinery requires large diameter bearings that can perform under extreme conditions. Vibrations and critical speeds of rotor supported by tilting pad journal bearing (TPJBs) exceeding their design limits may cause unit failure. This paper aims to investigate the experimental technique for large diameter bearings.

To obtain the experimental support for rotor-bearing system design, an experiment focusing on vibration monitoring is given. The sensors arrangement, monitoring system and critical speed identification method are provided.

By using test bench in factory unit, a large amount of vibrations data of different working situations is obtained. In addition, a method named non-excitation identification for critical speed is proposed. The critical speed of rotor identified through vibration data is given. The theoretical calculation results are also presented.

The basis for rotor-bearing system design can be obtained through comparisons between the experimental results and the theoretical calculation data.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and changing the ANN structure.

An algorithm is used to select the best subset of features to boost the success of detecting healthy and faulty ball. Some of the important parameters of the ANN are also optimized to make the classifier achieve the maximum performance.

It was found that better accuracy can be obtained for ANN with fewer inputs.

The method can be used for other machinery condition‐monitoring systems which are based on ANN.

The results are useful for bearing fault detection systems designers and quality check centers in bearing manufacturing companies.

The algorithm used in this research is faster than in previous studies. Changing ANN parameters improved the results. The system was examined using experimental data of ball‐bearings.

Different groove angles are used to study performance characteristics of two-axial groove journal bearing. In this study two grooves are located at ±90º to the load line. The various angles of grooves have been taken as 10° to 40° in the interval of 5°. Different equations such as Reynolds equation, three-dimensional energy equation and heat conduction equation have been solved using finite element method and finite difference method. Pressure distribution in fluid is found by using Reynolds equation. The three-dimensional energy equation is used for temperature distribution in the fluid film and bush. One-dimensional heat conduction equation is used for finding temperature in axial direction for journal. There is a very small effect of groove angle on film thickness, eccentricity ratio and pressure. There is a drastic change in attitude angle and side flow. Result shows that there is maximum power loss at large groove angle. So the smaller groove angle is recommended for two-axial groove journal bearing.

The finite element method is used for solving Reynolds equation for pressure distribution in fluid. The finite difference method is adopted for finding temperature distribution in bush, fluid and journal.

Pressure distribution in fluid is found out. Temperature distribution in bush, fluid and journal is found out. There is a very small effect of groove angle on film thickness, eccentricity ratio and pressure.

The groove angle used is from 10 to 40 degree. The power loss is more when angle of groove increases, so smaller groove angle is recommended for this study.

The location of groove angle predicts the distribution of pressure and temperature in journal bearing. It will show the performance characteristics. ±90° angle we will prefer that will get before manufacturing of bearing.

Due to this study, we will get predict how the pressure and temperature distribute in the journal. It will give the running condition of bearing as to at what speed and load we will get the maximum temperature and pressure in the bearing.

The finite element method is used for solving the Reynolds equation. Three-dimensional energy equation is solved using the finite difference method. Heat conduction equation is also solved for journal. The C language is used. The code is developed in C language. There are different equations which depend on each other. The temperature is dependent on pressure viscosity of fluid, etc. so C code is preferred.

An approach is presented for the development of a predictive maintenance system for rotor‐dynamic pumps, which focuses on the diagnosis of abnormal events related to fluid‐dynamic operating conditions. This methodology is based on an experimental characterization of the dynamic response of the pump under different loads and operation anomalies. The procedure has been put into practice on a medium‐sized centrifugal pump. The results obtained show that a simple spectral analysis of the pressure signals captured at either the inlet or the outlet of the pump can provide sufficient decision criteria to constitute the basis for a diagnostic system. This was not true however when analyzing signals of acceleration at the pump casing.

WHEN an airscrew leaves the factory, where it has been brought within close limits of perfect force, moment, and aerodynamic balance, the problem of maintaining it in smooth operation throughout its life is only partly solved. The precision of balance which should be met for optimum performance is higher than any piece of mechanism can preserve indefinitely even though it be judged perfect by ordinary standards. Surface abrasion of the blades, material lost in smoothing and polishing, changes in bearing clearance, even though insignificant from the standpoint of mechanical operation; these as well as wear on the centring surfaces of the airscrew shaft and cones may easily conspire to produce unbalance readily detectable in the aeroplane cabin.

This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.