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This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.

By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal.

The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method.

Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.

This paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.

It is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.

DTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.

Verification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

This paper aims to present a new fault detection and classification scheme of both DC faults and AC faults on a DC microgrid network.

To achieve reliable protection, the derivative of DC current signal is decomposed into several intrinsic modes using variational mode decomposition (VMD), which are then used as inputs to the Hilbert–Haung transform technique to obtain the instantaneous amplitude and frequency of the decomposed modes of the signal. A weighted Kurtosis index is used to obtain the most sensitive mode, which is used to compute sudden change in discrete Teager energy (DTE), indicating the occurrence of the fault. A stacked autoencoder-based neural network is applied for classifying the pole to ground (PG), pole to pole (PP), line to ground (LG), line to line (LL) and three-phase line to ground (LLLG) faults. The effectiveness of the proposed protection technique is validated in MATLAB/SIMULINK by considering different test cases.

As the maximum fault detection time is only 5 ms, the proposed detection technique is very fast. A stacked autoencoder-based neural network is applied for classifying the PG, PP, LG, LL and LLLG faults with classification accuracy of 99.1%.

The proposed technique provides a very fast, reliable and accurate protection scheme for DC microgrid system.

Most systems have a non-linear (NL) behavior and measured signals reflect this non-linearity such that in general they are composed with more than one sinusoidal component. NL analysis methods represent an option for analyzing such signals, however these methods have been developed for single frequency signals, this forces to implement a components separation procedure before performing the signal analysis. The purpose of this paper is to present a new method for analyzing multi-component signals that allows calculating amplitude, frequency and damping constants of the contained sinusoidal components. The method is able to simultaneously identify the different components within a detection bandwidth without previous separation of mono-components, as needed for most methods in used today.

The method proposed in this work characterizes sinusoidal signals determining their amplitude, frequency and damping constant. This method is based on transforming from the time domain to the z-domain an oscillatory signal that may or may not possess damping. Since frequency and damping of a signal can be determined knowing its z-domain poles, using the signal in z-transform domain an equations system to find the signal poles can be written.

From the results it can be concluded that the proposed method is reliable and consistent. One quality of the method is its short delay, when the procedure starts there is a delay equal to the time needed to accumulate four samples for each detectable frequency in order to perform the first calculation, after this, the algorithm can deliver a result at each sampling instant. This short delay and the low complexity of the algorithm can permit using the method in real time applications.

The proposed method is able to determine frequencies, damping constants and amplitudes of the components of a signal without a previous separation of mono-components, in contrast with other methods that require filter banks tuned using a previous knowledge of the signal. Moreover unlike techniques such as the Hilbert-Huang Transform the proposed method can be applied to signals with components having very close frequencies.

In structural health monitoring, localization of multiple slight damage without baseline data is significant and difficult. The purpose of this paper is to discuss these issues.

Damage in the structure causes singularities of displacement modes, which in turn reveals damage. Methods based on the displacement modes may fail to accurately locate the slight damage because the slight damage in engineering structure results in a relatively small variation of the displacement modes. In comparison with the displacement modes, the strain modes are more sensitive to the slight damage because the strain is the derivative of the displacement. As a result, the slight variation in displacement data will be magnified by the derivative, leading to a significant variation of the strain modes. A novel method based on strain modes is proposed for the purpose of accurately locating the multiple slight damage.

In the two bay beam and steel fixed-fixed beams, the numerical simulations and the experimental cases, respectively, illustrate that the proposed method can achieve more accurate localization in comparison with the one based on the displacement modes.

The paper offers a practical approach for more accurate localization of multiple slight damage without baseline data. And the robustness to measurement noise of the proposed method is evaluated for increasing levels of artificially added white Gaussian noise until its limit is reached, defining its range of practical applicability.

Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs).

This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic.

The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field.

Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

In this project, we propose and test a new device – wearable sociometric badges containing small microphones – as a low-cost and relatively unobtrusive tool for measuring stress response to group processes. Specifically, we investigate whether voice pitch, measured using the microphone of the sociometric badge, is associated with physiological stress response to group processes.

We collect data in a laboratory setting using participants engaged in two types of small-group interactions: a social interaction and a problem-solving task. We examine the association between voice pitch (measured by fundamental frequency of the participant’s speech) and physiological stress response (measured using salivary cortisol) in these two types of small-group interactions.

We find that in the social task, participants who exhibit a stress response have a statistically significant greater deviation in voice pitch (from their overall average voice pitch) than those who do not exhibit a stress response. In the problem-solving task, participants who exhibit a stress response also have a greater deviation in voice pitch than those who do not exhibit a stress response, however, in this case, the results are only marginally significant. In both tasks, among participants who exhibited a stress response, we find a statistically significant correlation between physiological stress response and deviation in voice pitch.

We conclude that wearable microphones have the potential to serve as cheap and unobtrusive tools for measuring stress response to group processes.

About 13 to 44 per cent of motor faults are caused by bearing failures in induction motors (IMs), where lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication. An excessive amount of grease causes the rollers or balls to slide along the race instead of turning, and the grease will actually churn. This churning action will eventually wear down the base oil of the grease and all that will be left to lubricate the bearing is a thickener system with little or no lubricating properties. The heat generated from the churning, insufficient lubricating oil will begin to harden the grease, and this will prevent any new grease added to the bearing from reaching the rolling elements, with the consequence of bearing failure and equipment downtime. Regarding the case of grease excess in bearings, this case has not been sufficiently studied. This work aims to present an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the Margenau-Hill distribution (MHD) and artificial neural networks (ANNs), where the obtained results demonstrate the correct classification of the studied cases.

This work proposed an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs.

In this paper, three cases of study for a bearing in an IM are studied, detected and classified correctly by combining some methods. The marginal frequency is obtained from the MHD, which in turn is achieved from the stator current signal, and a total of six features are estimated from the power spectrum, and these features are forwarded to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing.

The proposed methodology can be applied to other applications; it could be useful to use a time–frequency representation through the MHD for obtaining the energy density distribution of the signal frequency components through time for analysis, evaluation and identification of faults or conditions in the IM for example; therefore, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

The lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication and it negatively affects the efficiency of the motor, resulting in higher operating costs. Therefore, in this work, a new methodology is proposed for the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. The proposed methodology uses a total of six features estimated from the power spectrum, and these features are sent to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. From the obtained results, it was demonstrated that the proposed approach achieves higher classification performance, compared to short-time Fourier transform, Gabor transform and Wigner-Ville distribution methods, allowing to identify mechanical bearing faults and bearing excessively lubricated conditions in an IM, with a remarkable 100 per cent effectiveness during classification for treated cases. Also, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.

In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.

The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.

An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.