Hydrothermal synthesis of single crystallin strontium titanate nanocubes

Strontium titanate nanocubes with an average edge length of 150mm have been successfully synthesized from a simple hydrothermal system. Characterization techniques such as X-ray powder diffraction analysis, scanning electron microscopy and energy-dispersive analysis of X-rays were used to investigate the products. The results showed that as-prepared powders are pure SrTiO3 with cubic shape, which consists with the growth habit of its intrinsic crystal structure. These uniform nanocubes with high crystallinity will exhibit superior physical properties in the practical applications. Furthermore, during the experimental process, it has been found that the dilute acid washing process is very important to obtain high pure SrTiO3.

Renewal decision support for linear assets

Linear (or continuous) assets are engineering infrastructure that usually spans long distances and can be divided into different segments, all of which perform the same function but may be subject to different loads and environmental factors. Typical linear assets include railway lines, roads, pipelines and cables. How and when to renew such assets are critical decisions for asset owners as they normally involves significant capital investment. Through investigating the characteristics of linear asset renewal decisions and identifying the critical requirements that are associated with renewal decisions, we present a multi-criteria decision support method to help optimise renewal decisions. A case study that concerns renewal of an economiser's tubing system is a coal-fired power station is adopted to demonstrate the application of this method. Although the paper concerns a particular linear asset decision type, the approach has broad applicability for linear asset management.

Simulation of AE wave propagation in thin plate for source identification

In most materials, short stress waves are generated during the process of plastic deformation, phase transformation, crack formation and crack growth. These phenomena are applied in acoustic emission (AE) for the detection of material defects in wide spectrum areas, ranging from non-destructive testing for the detection of materials defects to monitoring of microeismical activity. AE technique is also used for defect source identification and for failure detection. AE waves consist of P waves (primary/longitudinal waves), S waves (shear/transverse waves) and Rayleight (surface) waves as well as reflected and diffracted waves. The propagation of AE waves in various modes has made the determination of source location difficult. In order to use the acoustic emission technique for accurate identification of source location, an understanding of wave propagation of the AE signals at various locations in a plate structure is essential. Furthermore, an understanding of wave propagation can also assist in sensor location for optimum detection of AE signals. In real life, as the AE signals radiate from the source it will result in stress waves. Unless the type of stress wave is known, it is very difficult to locate the source when using the classical propagation velocity equations. This paper describes the simulation of AE waves to identify the source location in steel plate as well as the wave modes. The finite element analysis (FEA) is used for the numerical simulation of wave propagation in thin plate. By knowing the type of wave generated, it is possible to apply the appropriate wave equations to determine the location of the source. For a single plate structure, the results show that the simulation algorithm is effective to simulate different stress waves.

Bringing the MIMOSA OSA-EAI into an object-orientated world

Despite being poised as a standard for data exchange for operation and maintenance data, the database heritage of the MIMOSA OSA-EAI is clearly evident from using a relational model at its core. The XML schema (XSD) definitions, which are used for communication between asset management systems, are based on the MIMOSA common relational information schema (CRIS), a relational model, and consequently, many database concepts permeate the communications layer. The adoption of a relational model leads to several deficiencies, and overlooks advances in object-oriented approach for an upcoming version of the specification, and the common conceptual object model (CCOM) sees a transition to fully utilising object-oriented features for the standard. Unified modelling language (UML) is used as a medium for documentation as well as facilitating XSD code generation. This paper details some of the decisions faced in developing the CCOM and provides a glimpse into the future of asset management and data exchange models.

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines.

Integrated approach for HP-LNG pump diagnosis and prognostics based on health state probability estimation

This paper presents an innovative prognostics model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system. To employ an appropriate classifier for health state probability estimation in the proposed prognostic model, the comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault levels of three faults in HP-LNG pump. Two sets of impeller-rubbing data were employed for the prediction of pump remnant life based on estimation of discrete health state probability using an outstanding capability of SVM and a feature selection technique. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

A study on the use of acoustic emission technique as a structural health monitoring tool

As civil infrastructures such as bridges age, there is a concern for safety and a need for cost-effective and reliable monitoring tool. Different diagnostic techniques are available nowadays for structural health monitoring (SHM) of bridges. Acoustic emission is one such technique with potential of predicting failure. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). AEtechnique involves recording the stress waves bymeans of sensors and subsequent analysis of the recorded signals,which then convey information about the nature of the source. AE can be used as a local SHM technique to monitor specific regions with visible presence of cracks or crack prone areas such as welded regions and joints with bolted connection or as a global technique to monitor the whole structure. Strength of AE technique lies in its ability to detect active crack activity, thus helping in prioritising maintenance work by helping focus on active cracks rather than dormant cracks. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. One is the generation of large amount of data during the testing; hence an effective data analysis and management is necessary, especially for long term monitoring uses. Complications also arise as a number of spurious sources can giveAEsignals, therefore, different source discrimination strategies are necessary to identify genuine signals from spurious ones. Another major challenge is the quantification of damage level by appropriate analysis of data. Intensity analysis using severity and historic indices as well as b-value analysis are some important methods and will be discussed and applied for analysis of laboratory experimental data in this paper.

Diesel knock combustion and its detection using acoustic emission

This paper presents an experimental investigation into the detection of excessive Diesel knock using acoustic emission signals. Three different dual-fuel Diesel engine operating regimes were induced into a compression ignition (Diesel) engine operating on both straight Diesel fuel and two different mixtures of fumigated ethanol and Diesel. The experimentally induced engine operating regimes were; normal, or Diesel only operation, acceptable dual-fuel operation and dual-fuel operation with excessive Diesel knock. During the excessive Diesel knock operating regime, high rates of ethanol substitution induced potentially damaging levels of Diesel knock. Acoustic emission data was captured along with cylinder pressure, crank-angle encoder, and top-dead centre signals for the different engine operating regimes. Using these signals, it was found that acoustic emission signals clearly distinguished between the two acceptable operating regimes and the operating regime experiencing excessive Diesel knock. It was also found that acoustic emission sensor position is critical. The acoustic emission sensor positioned on the block of the engine clearly related information concerning the level of Diesel knock occurring in the engine whist the sensor positioned on the head of the engine gave no indication concerning Diesel knock severity levels.

Diesel engine problems, acoustic emission signals and simulated misfire faults

This paper discusses commonly encountered diesel engine problems and the underlying combustion related faults. Also discussed are the methods used in previous studies to simulate diesel engine faults and the initial results of an experimental simulation of a common combustion related diesel engine fault, namely diesel engine misfire. This experimental fault simulation represents the first step towards a comprehensive investigation and analysis into the characteristics of acoustic emission signals arising from combustion related diesel engine faults. Data corresponding to different engine running conditions was captured using in-cylinder pressure, vibration and acoustic emission transducers along with both crank-angle encoder and top-dead centre signals. Using these signals, it was possible to characterise the diesel engine in-cylinder pressure profiles and the effect of different combustion conditions on both vibration and acoustic emission signals.

Condition deterioration prediction of bridge elements using Dynamic Bayesian Networks (DBNs)

The ability of bridge deterioration models to predict future condition provides significant advantages in improving the effectiveness of maintenance decisions. This paper proposes a novel model using Dynamic Bayesian Networks (DBNs) for predicting the condition of bridge elements. The proposed model improves prediction results by being able to handle, deterioration dependencies among different bridge elements, the lack of full inspection histories, and joint considerations of both maintenance actions and environmental effects. With Bayesian updating capability, different types of data and information can be utilised as inputs. Expert knowledge can be used to deal with insufficient data as a starting point. The proposed model established a flexible basis for bridge systems deterioration modelling so that other models and Bayesian approaches can be further developed in one platform. A steel bridge main girder was chosen to validate the proposed model.

Maintenance optimisation of a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals

This paper presents a maintenance optimisation method for a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals. The objective function considered in the paper is the average revenue per unit time calculated based on the semi-regenerative theory and the universal generating function (UGF). A new algorithm using the stochastic ordering is also developed in this paper to reduce the search space of maintenance strategies and to enhance the efficiency of optimisation algorithms. A numerical simulation is presented in the study to evaluate the efficiency of the proposed maintenance strategy and optimisation algorithms. The simulation result reveals that maintenance strategies with opportunistic maintenance and state-dependent inspection intervals are more cost-effective when the influence of economic dependence and inspection cost is significant. The study further demonstrates that the optimisation algorithm proposed in this paper has higher computational efficiency than the commonly employed heuristic algorithms.

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.

Optimising preventive maintenance strategy for production Lines

Preventive Maintenance (PM) is often applied to improve the reliability of production lines. A Split System Approach (SSA) based methodology is presented to assist in making optimal PM decisions for serial production lines. The methodology treats a production line as a complex series system with multiple (imperfect) PM actions over multiple intervals. The conditional and overall reliability of the entire production line over these multiple PM intervals are hierarchically calculated using SSA, and provide a foundation for cost analysis. Both risk-related cost and maintenance-related cost are factored into the methodology as either deterministic or random variables. This SSA based methodology enables Asset Management (AM) decisions to be optimised considering a variety of factors including failure probability, failure cost, maintenance cost, PM performance, and the type of PM strategy. The application of this new methodology and an evaluation of the effects of these factors on PM decisions are demonstrated using an example. The results of this work show that the performance of a PM strategy can be measured by its Total Expected Cost Index (TECI). The optimal PM interval is dependent on TECI, PM performance and types of PM strategies. These factors are interrelated. Generally, it was found that a trade-off between reliability and the number of PM actions needs to be made so that one can minimise Total Expected Cost (TEC) for asset maintenance.