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The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

To make wind energy (one of the alternative-energy production technologies) economical, wind-turbines (convertors of wind energy into electrical energy) are required to operate, with only regular maintenance, for at least 20 years. However, some key wind-turbine components (especially the gear-box) often require significant repair or replacement after only three to five years in service. This causes an increase in both the wind-energy cost and the cost of ownership of the wind turbine. The paper aims to discuss these issues.

To overcome this problem, root causes of the gear-box premature failure are currently being investigated using mainly laboratory and field-test experimental approaches. As demonstrated in many industrial sectors (e.g. automotive, aerospace, etc.) advanced computational engineering methods and tools cannot only complement these experimental approaches but also provide additional insight into the problem at hand (and do so with a substantially shorter turn-around time). The present work demonstrates the use of a multi-length-scale computational approach which couples large-scale wind/rotor interactions with a gear-box dynamic response, enabling accurate determination of kinematics and kinetics within the gear-box bearings (the components most often responsible for the gear-box premature failure) and ultimately the structural response (including damage and failure) of the roller-bearing components (e.g. inner raceways).

It has been demonstrated that through the application of this approach, one can predict the expected life of the most failure-prone horizontal axis wind turbine gear-box bearing elements.

To the authors’ knowledge, the present work is the first multi-length-scale study of bearing failure in wind-turbines.

A simple economic analysis has revealed that in order for wind energy to be a viable alternative, wind-turbines (convertors of wind energy into electrical energy) must be able to operate for at least 20 years, with only regular maintenance. However, wind-turbines built nowadays do not generally possess this level of reliability and durability. Specifically, due to the malfunction and failure of drive-trains/gear-boxes, many wind-turbines require major repairs after only three to five years in service. The paper aims to discuss these issues.

The subject of the present work is the so-called white etch cracking, one of the key processes responsible for the premature failure of gear-box roller-bearings. To address this problem, a multi-physics computational methodology is developed and used to analyze the problem of wind-turbine gear-box roller-bearing premature-failure. The main components of the proposed methodology include the analyses of: first, hydrogen dissolution and the accompanying grain-boundary embrittlement phenomena; second, hydrogen diffusion from the crack-wake into the adjacent unfractured material; third, the inter-granular fracture processes; and fourth, the kinematic and structural response of the bearing under service-loading conditions.

The results obtained clearly revealed the operation of the white-etch cracking phenomenon in wind-turbine gear-box roller-bearings and its dependence on the attendant loading and environmental conditions.

The present work attempts to make a contribution to the resolution of an important problem related to premature-failure and inferior reliability of wind-turbine gearboxes.

The purpose of this paper is to develop accurate model and simulation of mechanical power transmission within roller‐screw electromechanical actuators with special attention to friction, compliance and inertia effects. Also, to propose non‐intrusive experiments for the identification of model parameters with an integrator or system‐oriented view.

At system design level, the actuation models need to reproduce with confidence the energy losses and the main dynamic effects. The adopted modelling methodology is based on non‐intrusive measurements taken on a standard actuator test‐bench. The actuator model is first structured with respect to the bond‐graph formalism that allows a clear identification of the considered effects and associated causalities for model implementation. Various approaches are then combined, mixing blocked or moving load, position or torque control and time or frequency domains analysis. The friction representation model is suggested using a step‐by‐step approach that covers a wide domain of operation. The model is validated under varying torque and speed conditions.

A structured model is introduced with support of the bond‐graph formalism. Combining blocked/moving load and time/frequency domain experiments allows the development of progressive model identification. An advanced friction representation model is proposed including the effects of speed, transmitted force, quadrant of operation and roller‐screw preload.

Mechanical transmission energy losses and dynamics are modelled in a system‐oriented view without massive need to confidential design parameters. Not only speed but also load and operation quadrant effects are reproduced by the proposed friction model. The non‐intrusive experimental procedure is made consistent with use of a standard actuator test‐bench.

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.

This paper aims to analyze fatigue failures of a typical marine gearbox under harsh ocean conditions, and these failures are reasonably attributed to the bearing fretting fatigue damages.

Two typical FAG cylindrical roller bearings mounted on this marine gearbox are particularly used for analysis, as they are most vulnerable to these failures. A series of simulations have also been conducted to verify the analysis results and failure reasons by reproducing the fretting fatigue damages for the same shaft-bearing system under the same manufacturing error conditions.

The analysis results indicate that manufacturing errors are the most possible reasons for the bearing failures, and these errors have more effects on the FAG cylindrical roller bearing as compared to other bearings mounted on the same shaft system. The simulations results are in good agreement with the theoretical analysis results and test results and hence validate that manufacturing errors are the dominant reasons for bearing fretting fatigue damages in this typical marine gearbox.

Fatigue failures of a typical marine gearbox. Manufacturing errors are the most possible reasons for the bearing failures. A series of simulations have been conducted to verify the analysis results and failure reasons. The simulations results are in good agreement with the theoretical analysis results and test results.

The Presidential Address to the Liverpool Engineering Society by Mr. Farthing (the salient points of which are reproduced in this issue) has particular bearing upon lubrication and especially on young lubrication engineers. Mr. Farthing stressed the very wide field open to young engineers and the difficulties associated with training in order to cover as wide a field as may be necessary. It is usually so important to gain a wide knowledge before one can specialise and this is certainly the case with lubrication engineers. One cannot begin to fully appreciate the intricacies of a lubrication system with all its accessory components lubricating and guarding, for example, a large motive power plant or rolling mill, until one has more than a mere working knowledge of the plant itself, the duties it must perform, how it performs them and the snags that arise which might be overcome by correct lubrication. In view of the fact that lubrication systems are just as important in a textile mill as in a power station or a large brick works, the almost impossible‐to‐achieve‐range of knowledge that would simplify the work of a lubrication engineer is very obvious. Fortunately, lubricating principles apply to most cases and knowing how to apply one's knowledge from basic principles is the key to success in this difficult profession.

New, undestroyed greases of the same brands as those used in a field test, described in Part 1, were examined using conventional methods, such as the SKF V2F test, the roll stability test (ASTM D‐1831), the Grease Worker (ASTM D‐217), the torque test (ASTM 1478‐91), bleeding measurements ( IP 121), yield stress measurements, the 4‐ball test (ASTM D 2266‐86), base oil viscosity measurements, thickener content and the cone penetration test (ASTM D217‐88). The greases have also been tested with several new test methods developed at the University. A specification for relevant testing methods was drawn up and the connections between the tested parameters were investigated. It was found that the mechanical stability could be predicted with a combination of ASTM D‐1831 and the limiting shear stress coefficient γ. This coefficient is capable of predicting wear. It was found that the bearing temperature could be predicted by using the base oil viscosity.

SELECTING the correct bearing for any particular application involves more than the determination of the correct type and size. It is true that the calculation of the nominal working life will give an indication of the operational life before failure, but this calculation only takes into account the fatigue life of the material. If this theoretical life is to be obtained—and perhaps exceeded—then additional factors must be taken into account when initially designing the bearing arrangement. Lubrication and protection from the operating environment are two very important considerations.

The aim of this paper is to develop a contribution to knowledge that adds to the empirical evidence of predictive condition-based maintenance by demonstrating how the availability and reliability of current assets can be improved without costly capital investment, resulting in overall system performance improvements

The empirical, experimental approach, technical action research (TAR), was designed to study a major Middle Eastern airport baggage handling operation. A predictive condition-based maintenance prototype station was installed to monitor the condition of a highly complex system of static and moving assets.

The research provides evidence that the performance frontier for airport baggage handling systems can be improved using automated dynamic monitoring of the vibration and digital image data on baggage trays as they pass a service station. The introduction of low-end innovation, which combines advanced technology and low-cost hardware, reduced asset failures in this complex, high-speed operating environment.

The originality derives from the application of existing hardware with the combination of edge and cloud computing software through architectural innovation, resulting in adaptations to an existing baggage handling system within the context of a time-critical logistics system.