Ability of modal analysis to detect osseointegration of implants in transfemoral amputees : a physical model study

Owing to the successful use of non-invasive vibration analysis to monitor the progression of dental implant healing and stabilization, it is now being considered as a method to monitor femoral implants in transfemoral amputees. This study uses composite femur-implant physical models to investigate the ability of modal analysis to detect changes at the interface between the implant and bone simulating those that occur during osseointegration. Using electromagnetic shaker excitation, differences were detected in the resonant frequencies and mode shapes of the model when the implant fit in the bone was altered to simulate the two interface cases considered: firm and loose fixation. The study showed that it is beneficial to examine higher resonant frequencies and their mode shapes (rather than the fundamental frequency only) when assessing fixation. The influence of the model boundary conditions on the modal parameters was also demonstrated. Further work is required to more accurately model the mechanical changes occurring at the bone-implant interface in vivo, as well as further refinement of the model boundary conditions to appropriately represent the in vivo conditions. Nevertheless, the ability to detect changes in the model dynamic properties demonstrates the potential of modal analysis in this application and warrants further investigation.

Preface to special issue on digital human modelling for vehicle design and manufacturing

The automotive industry has been the focus of digital human modeling (DHM) research and application for many years. In the highly competitive marketplace for personal transportation, the desire to improve the customer’s experience has driven extensive research in both the physical and cognitive interaction between the vehicle and its occupants. Human models provide vehicle designers with tools to view and analyze product interactions before the first prototypes are built, potentially improving the design while reducing cost and development time. The focus of DHM research and applications began with prediction and representation of static postures for purposes of driver workstation layout, including assessments of seat adjustment ranges and exterior vision. Now DHMs are used for seat design and assessment of driver reach and ingress/egress. DHMs and related simulation tools are expanding into the cognitive domain, with computational models of perception and motion, and into the dynamic domain with models of physical responses to ride and vibration. Moreover, DHMs are now widely used to analyze the ergonomics of vehicle assembly tasks. In this case, the analysis aims to determine whether workers can be expected to complete the tasks safely and with good quality. This preface provides a review of the literature to provide context for the nine new papers presented in this special issue.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Signal patterns of piston slap of a four-cylinder diesel engine

This paper presents an experimental study on the vibration signal patterns associated with a simulated piston slap test of a four-cylinder diesel engine. It is found that a simulated worn-off piston results in an increase in vibration RMS peak amplitudes associated with the major mechanical events of the corresponding cylinder (i.e., inlet and exhaust valve closing and combustion of Cylinder 1). This then led to an increase of overall vibration amplitude of the time domain statistical features such as RMS, Crest Factor, Skewness and Kurtosis in all loading conditions. The simulated worn-off piston not only increased the impact amplitude of piston slap during the engine combustion, it also produced a distinct impulse response during the air induction stroke of the cylinder attributing to an increase of lateral impact force as a result of piston reciprocating motion and the increased clearance between the worn-off piston and the cylinder. The unique signal patterns of piston slap disclosed in this paper can be utilized to assist in the development of condition monitoring tools for automated diagnosis of similar diesel engine faults in practical applications.

The effect of ribbing on the energy flow between two rectangular plates coupled at right angle

This paper presents an analytical model to study the effect of stiffening ribs on vibration transmission between two rectangular plates coupled at right angle. Interesting wave attenuation patterns were observed by placing the stiffening rib either on the source or on the receiving plate. The result can be used to improve the understanding of vibration and for vibration control of more complex structures such as transformer tanks and machine covers.

Innovative hybrid composite floor plate system

New materials technology has provided the potential for the development of an innovative Hybrid Composite Floor Plate System (HCFPS) with many desirable properties, such as light weight, easy to construct, economical, demountable, recyclable and reusable. Component materials of HCFPS include a central Polyurethane (PU) core, outer layers of Glass-fibre Reinforced Cement (GRC) and steel laminates at tensile regions. HCFPS is configured such that the positive inherent properties of individual component materials are combined to offset any weakness and achieve optimum performance. Research has been carried out using extensive Finite Element (FE) computer simulations supported by experimental testing. Both the strength and serviceability requirements have been established for this lightweight floor plate system. This paper presents some of the research towards the development of HCFPS along with a parametric study to select suitable span lengths.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Numerical study of impact forces on railway sleepers under wheel flat

Wheel-rail interaction is one of the most important research topics in railway engineering. It includes track vibration, track impact response and safety of the track. Track structure failures caused by impact forces can lead to significant economic loss for track owners through damage to rails and to the sleepers beneath. The wheel-rail impact forces occur because of imperfections on the wheels or rails such as wheel flats, irregular wheel profile, rail corrugation and differences in the height of rails connected at a welded joint. The vehicle speed and static wheel load are important factors of the track design, because they are related to the impact forces under wheel-rail defects. In this paper, a 3-Dimensional finite element model for the study of wheel flat impact is developed by use of the FEA software package ANSYS. The effects of the wheel flat to impact force on sleepers with various speeds and static wheel loads under a critical wheel flat size are investigated. It has found that both wheel-rail impact force and impact force on sleeper induced by wheel flat are varying nonlinearly by increasing the vehicle speed; both impact forces are nonlinearly and monotonically increasing by increasing the static wheel load. The relationships between both of impact forces induced by wheel flat and vehicles speed or static load are important to the track engineers to improve the design and maintenance methods in railway industry.

A dynamic wheel-rail impact analysis of railway track under wheel flat by Finite Element Analysis

Wheel–rail interaction is one of the most important research topics in railway engineering. It involves track impact response, track vibration and track safety. Track structure failures caused by wheel–rail impact forces can lead to significant economic loss for track owners through damage to rails and to the sleepers beneath. Wheel–rail impact forces occur because of imperfections in the wheels or rails such as wheel flats, irregular wheel profiles, rail corrugations and differences in the heights of rails connected at a welded joint. A wheel flat can cause a large dynamic impact force as well as a forced vibration with a high frequency, which can cause damage to the track structure. In the present work, a three-dimensional (3-D) finite element (FE) model for the impact analysis induced by the wheel flat is developed by use of the finite element analysis (FEA) software package ANSYS and validated by another validated simulation. The effect of wheel flats on impact forces is thoroughly investigated. It is found that the presence of a wheel flat will significantly increase the dynamic impact force on both rail and sleeper. The impact force will monotonically increase with the size of wheel flats. The relationships between the impact force and the wheel flat size are explored from this finite element analysis and they are important for track engineers to improve their understanding of the design and maintenance of the track system.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Influence of grain boundaries on the vibrational properties of silver nanowires

Metal and semiconductor nanowires (NWs) have been widely employed as the building blocks of the nanoelectromechanical systems, which usually acted a resonant beam. Recent researches reported that nanowires are often polycrystalline, which contains grain boundaries (GBs) that transect the whole nanowire into a bamboo like structure. Based on the larger-scale molecular dynamics (MD) simulations, a comprehensive investigation of the influence from grain boundaries on the vibrational properties of doubly clamped Ag NWs is conducted. It is found that, the presence of grain boundary will result in significant energy dissipation during the resonance of polycrystalline NWs, which leads a great deterioration to the quality factor. Further investigation reveals that the energy dissipation is originated from the plastic deformation of polycrystalline NWs in the form of the nucleation of partial dislocations or the generation of micro stacking faults around the GBs and the micro stacking faults is found to keep almost intact during the whole vibration process. Moreover, it is observed that the closer of the grain boundary getting to the regions with the highest strain state, the more energy dissipation will be resulted from the plastic deformation. In addition, either the increase of the number of grain boundaries or the decrease of the distance between the grain boundary and the highest strain state region is observed to induce a lower first resonance frequency. This work sheds lights on the better understanding of the mechanical properties of polycrystalline NWs, which benefits the increasing utilities of NWs in diverse nano-electronic devices.

Assessment of the molecular structure of the borate mineral boracite Mg3B7O13Cl using vibrational spectroscopy

Boracite is a magnesium borate mineral with formula: Mg3B7O13Cl and occurs as blue green, colorless, gray, yellow to white crystals in the orthorhombic – pyramidal crystal system. An intense Raman band at 1009 cm−1 was assigned to the BO stretching vibration of the B7O13 units. Raman bands at 1121, 1136, 1143 cm−1 are attributed to the in-plane bending vibrations of trigonal boron. Four sharp Raman bands observed at 415, 494, 621 and 671 cm−1 are simply defined as trigonal and tetrahedral borate bending modes. The Raman spectrum clearly shows intense Raman bands at 3405 and 3494 cm−1, thus indicating that some Cl anions have been replaced with OH units. The molecular structure of a natural boracite has been assessed by using vibrational spectroscopy.

Infrared and Raman spectroscopic characterization of the phosphate mineral kosnarite KZr2(PO4)3 in comparison with other pegmatitic phosphates

In this research, we have used vibrational spectroscopy to study the phosphate mineral kosnarite KZr2(PO4)3. Interest in this mineral rests with the ability of zirconium phosphates (ZP) to lock in radioactive elements. ZP have the capacity to concentrate and immobilize the actinide fraction of radioactive phases in homogeneous zirconium phosphate phases. The Raman spectrum of kosnarite is characterized by a very intense band at 1,026 cm−1 assigned to the symmetric stretching vibration of the PO4 3− ν1 symmetric stretching vibration. The series of bands at 561, 595 and 638 cm−1 are assigned to the ν4 out-of-plane bending modes of the PO4 3− units. The intense band at 437 cm−1 with other bands of lower wavenumber at 387, 405 and 421 cm−1 is assigned to the ν2 in-plane bending modes of the PO4 3− units. The number of bands in the antisymmetric stretching region supports the concept that the symmetry of the phosphate anion in the kosnarite structure is preserved. The width of the infrared spectral profile and its complexity in contrast to the well-resolved Raman spectrum show that the pegmatitic phosphates are better studied with Raman spectroscopy.

Dynamic performance characteristics of an innovative Hybrid Composite Floor Plate System under human-induced loads

This study explored the dynamic performance of an innovative Hybrid Composite Floor Plate System (HCFPS), composed of Polyurethane (PU) core, outer layers of Glass–fibre Reinforced Cement (GRC) and steel laminates at tensile regions, using experimental testing and Finite Element (FE) modelling. Experimental testing included heel impact and walking tests for 3200 mm span HCFPS panels. FE models of the HCFPS were developed using the FE program ABAQUS and validated with experimental results. HCFPS is a light-weight high frequency floor system with excellent damping ratio of 5% (bare floor) due to the central PU core. Parametric studies were conducted using the validated FE models to investigate the dynamic response of the HCFPS and to identify characteristics that influence acceleration response under human induced vibration in service. This vibration performance was compared with recommended acceptable perceptibility limits. The findings of this study show that HCFPS can be used in residential and office buildings as a light-weight floor system, which does not exceed the perceptible thresholds due to human induced vibrations.

Raman spectroscopic study of the hydroxy-phosphate mineral plumbogummite PbAl3(PO4)2(OH,H2O)6

Plumbogummite PbAl3(PO4)2(OH,H2O)6 is a mineral of environmental significance and is a member of the alunite-jarosite supergroup. The molecular structure of the mineral has been investigated by Raman spectroscopy. The spectra of different plumbogummite specimens differ although there are many common features. The Raman spectra prove the spectral profile consisting of overlapping bands and shoulders. Raman bands and shoulders observed at 971, 980, 1002 and 1023 cm−1 (China sample) and 913, 981, 996 and 1026 cm−1 (Czech sample) are assigned to the ν1 symmetric stretching modes of the (PO4)3−, at 1002 and 1023 cm−1 (China) and 996 and 1026 cm−1 to the ν1 symmetric stretching vibrations of the (O3POH)2− units, and those at 1057, 1106 and 1182 (China) and at 1102, 1104 and 1179 cm−1 (Czech) to the ν3 (PO4)3− and ν3 (PO3) antisymmetric stretching vibrations. Raman bands and shoulders at 634, 613 and 579 cm−1 (China) and 611 and 596 cm−1 (Czech) are attributed to the ν4 (δ) (PO4)3− bending vibrations and those at 507, 494 and 464 cm−1 (China) and 505 and 464 cm−1 (Czech) to the ν2 (δ) (PO4)3− bending vibrations. The Raman spectrum of the OH stretching region is complex. Raman bands and shoulders are identified at 2824, 3121, 3249, 3372, 3479 and 3602 cm−1 for plumbogummite from China, and at 3077, 3227, 3362, 3480, 3518 and 3601 cm−1 for the Czech Republic sample. These bands are assigned to the ν OH stretching modes of water molecules and hydrogen ions. Approximate O–H⋯O hydrogen bond lengths inferred from the Raman spectra vary in the range >3.2–2.62 Å (China) and >3.2–2.67 Å (Czech). The minority presence of some carbonate ions in the plumbogummite (China sample) is connected with distinctive intensity increasing of the Raman band at 1106 cm−1, in which may participate the ν1 (CO3)2− symmetric stretching vibration overlapped with phosphate stretching vibrations.