Search results

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.

The purpose of this paper is to extend the learning boundaries of traditional project capability, which follows the linear planning paradigm, in order to include non-linear complex projects that cannot be completely specified and planned in advance, and so require continuous learning over their life cycles.

Based on an earlier empirical investigation, where complex-project capability (CPC) is developed through dynamic organizational learning based on non-linear problem solving, the paper examines some of the conceptual and practical implications of this process insight. The focus here is on incomplete pre-given knowledge and emergent knowledge creation during CPC development.

Using the three interrelated dimensions of project type, knowledge creation method, and organizational learning approach, the paper reinterprets Karl Popper’s linear problem solving model for learning in traditional projects by introducing the concept of knowledge entropy (disorder) for learning in non-linear complex projects. The latter follows a path from “order to disorder to order” rather than from “order to order” under traditional assumptions.

By identifying a common learning process at individual, group, and organizational levels, developing CPC can be viewed as a “learning organization”. This multi-level approach facilitates research into distributed organizing for emergent knowledge creation during CPC development.

In contrast to traditional planned projects with up-front prior knowledge, complex projects are characterized by incomplete knowledge. The challenge of dealing with knowledge uncertainty in complex projects through continuous learning has practical implications for project learning, planning, knowledge management, and leadership.

The concept of knowledge entropy (disorder) extends the learning boundaries of traditional projects, where little learning is anticipated, by including complex projects with knowledge uncertainty requiring continuous learning.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

We replicate and extend the social history treatment of the Berg, Dickhaut, and McCabe (1995) investment game, to further document how the reporting of financial history influences how laboratory societies organize themselves over time. We replicate Berg et al. (1995) by conducting a No History and a Financial History session to determine whether a report summarizing the financial transactions of a previous experimental session will significantly reduce entropy in the amounts sent by Investors and returned by Stewards in the investment game, as Berg et al. (1995) found. We extend Berg et al. (1995) in two ways. First, we conduct a total of five sessions (one No History and four Financial History sessions). Second, we introduce Shannon’s (1948) measure of entropy from information theory to assess whether the introduction of financial transaction history reduces the amount of dispersion in the amounts invested and returned across generations of players. Results across sessions indicate that entropy declined in both the amounts sent by Investors and the percentage returned by Stewards, but these patterns are weaker and mixed compared to those in the Berg et al. (1995) study. Additional research is needed to test how initial conditions, path dependencies, actors’ strategic reasoning about others’ behavior, multiple sessions, and communication may mediate the impact of financial history. The study’s multiple successive Financial History sessions and entropy measure are new to the investment game literature.

This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM).

The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment.

Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close.

As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

Aiming to provide a common theoretical foundation for all known biblio‐metric laws, the author starts from a systemic view of the information transfer process and assimilates it with a physical diffusion process, in particular the conduction of heat in solids. Previous literature induces in the properly ranked space of new authors an interest potential (temperature) confirmed by exchange of reference‐citation pairs, and driving a controlled information flow. The model gives its distribution for given initial and borderline conditions, allowing at the same time the establishment of new definitions for informational energy and entropy, which are coherent with the corresponding physical ones. The theory shows that impulsory energy supply is bound to negative entropy inflow which brakes the normal entropy ‘production’ in the ‘dissipative’ structures of the considered system. In this way the introduction of information into concrete thermodynamic systems analysis can hopefully be expected.

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

The purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.

In this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.

The numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.

The paper provides a useful reference for further theoretical research and engineering application.

In this work we perform a detailed entropy analysis of some substitutive sequences using the technique of lumping. The basic novelty of the entropy analysis by lumping is that, unlike the Fourier transform or the conventional entropy analysis by gliding, it gives results that can be related to algorithmic aspects of the sequences and in particular with the important property of automaticity. All computations in this paper have been performed with TOOLS FOR SYMBOLIC DYNAMICS a Maple package developed by the authors.

The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery.

In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures.

In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases.

The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management.

After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.