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The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.

The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.

Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.

A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.

The purpose of this paper is to report the findings of a study that used measurements of shaft relative rotational position, made using inexpensive Hall Effect sensors and magnets mounted at the ends of a gearbox input and output shafts, to determine gear “transmission variance.” The transmission variance signals, as a function of gear/shaft rotational position, were then used to detect and diagnose gear faults.

Two sets of spur gears (one plastic and one steel) were used to experimentally determine the relative shaft rotational position between the input and the output gearbox shafts. Fault-free and faulted (damaged tooth faces and cracked tooth bases) gears were used to collect representative dynamic signals. Signal processing was used to extract transmission variance values as a function of shaft rotational position and then used to detect and diagnose gear faults.

The results show that variations in the relative rotational position of the output shaft relative to that of the input shaft (the transmission variance) can be used to reveal gear mesh characteristics, including faults, such as cracked or missing gear teeth and flattened gear tooth faces, in both plastic gears and steel gears under appropriate (realistic) loads and speeds.

The operational mode of the non-contact rotational position sensors and the dynamic accuracy limitations are explained along with the basic signal processing required to extract transmission variance values.

The results show that shaft rotational position measurements can be made accurately and precisely using relatively inexpensive sensors and can subsequently reveal gear faults.

The inexpensive and yet trustworthy fault detection methodology developed in this work should help to improve the efficiency of maintenance actions on gearboxes and, therefore, improve the overall industrial efficiency of society.

The method described has distinct advantages over traditional analysis methods based on gearbox vibration and/or oil analysis.

Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar.

CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI).

The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers.

The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

In this paper, condition monitoring of the internal parts of a radial active magnetic bearing (AMB) system is performed by means of Cepstrum analysis, Wigner-Ville Distributions (WVD) and enveloped Equi-Sampled Discrete Fourier transforms (ESDFT). Sensor faults, power amplifier failures and controller faults were induced in both the simulation and physical AMB system. Condition monitoring by means of the abovementioned techniques were then performed on the displacement and current signals of the simulation and physical AMB system. Results were compared and conclusions were made on how effective Cepstrum analysis, WVD and enveloped ESDFT were on the faults induced on the AMB system.

Condition monitoring is an important aspect of the maintenance program for rotating machines. The vibration signature is usually analyzed to perform diagnostics of the health of the machine. The data acquisition and analysis were cumbersome in the analog era and dedicated instruments were required and the diagnostics was usually a laboratory‐based exercise and time consuming. With the advent of high‐speed microprocessors, the practice has completely changed. A modern continuous condition monitoring and diagnostics system is first described. With the recent advances in Internet‐based technologies, the condition monitoring procedures are poised for a quantum jump in the way the machines can be maintaned continuously on‐line at remote locations or checked through the Web for any faults. A specific example of the design of an Applet towards this goal is described in this paper.

Acoustic pattern recognition has shown itself to be a powerful inspection aid.

To better understand contributing factors and mediating mechanisms related to team dynamics in isolated, confined, and extreme (ICE) environments.

Literature review.

Our primary focus is on cohesion and adaptation – two critical aspects of team performance in ICE environments that have received increased attention in both the literature and funding initiatives. We begin by describing the conditions that define ICE environments and review relevant individual biological, neuropsychiatric, and environmental factors that interact with team dynamics. We then outline a unifying team cohesion framework for long-duration missions and discuss several environmental, operational, organizational, and psychosocial factors that can impact team dynamics. Finally, we end with a discussion of directions for future research and countermeasure development, emphasizing the importance of temporal dynamics, multidisciplinary integration, and novel conceptual frameworks for the inherently mixed work and social setting of long-duration missions in ICE environments.

A better understanding of team dynamics over time can contribute to success in a variety of organizational settings, including space exploration, defense and security, business, education, athletics, and social relationships.

We promote a multidisciplinary approach to team dynamics in ICE environments that incorporates dynamic biological, behavioral, psychological, and organizational factors over time.

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.

This paper seeks to propose a new non‐intrusive method for the assessment of speech quality of voice communication systems and evaluate its performance.

The method is based on measuring perception‐based objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre‐formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into equivalent subjective mean opinion scores (MOSs). The required clustering and matching processes are achieved by an efficient data‐mining tool known as the self‐organizing map (SOM). The proposed method was examined using a wide range of distortion including speech compression, wireless channel impairments, VoIP channel impairments, and modifications to the signal from features such as AGC.

The experimental results reported indicate that the proposed method provides a high level of accuracy in predicting the actual subjective quality of the speech. Specifically, the second version of the method, which is based on the use of bark spectrum (BS) analysis, is more accurate in predicting the MOS scores compared with its first and third versions (which are based on BS analysis and mel frequency cepstrum coefficients (MFCC), respectively), and outperforms the ITU‐T PESQ in a large number of test cases, particularly those related to distortion caused by channel impairments and signal level modifications.

It is believed that the prototype developed of the proposed objective speech quality measure is sufficiently accurate and robust against speaker, utterance and distortion type variations. Nevertheless, there are still possible directions for further improvements and enhancement. In general there are three areas that could be pursued for further improvements: widening the coverage of speaker variations of the system's codebook; formulating and using a perceptual speech model that provides true speaker‐independent representation of speech; and implementing the proposed measure as a stand‐alone system, preferably for real‐time applications.

Being an output‐based method, the proposed method can be employed for monitoring and assessing telecommunications networks under both live traffic conditions and off‐line evaluation.

The main contribution of this paper is the introduction of a new output‐based, non‐intrusive method for the assessment of speech quality that is sufficiently accurate and robust. To the best of the author's knowledge, no reliable output‐based objective speech quality assessment method has to date been reported or formally recognised.