Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure 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 for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Rover autonomous integrity monitoring of GNSS RTK positioning solutions with multi-constellations

This paper studies receiver autonomous integrity monitoring (RAIM) algorithms and performance benefits of RTK solutions with multiple-constellations. The proposed method is generally known as Multi-constellation RAIM -McRAIM. The McRAIM algorithms take advantage of the ambiguity invariant character to assist fast identification of multiple satellite faults in the context of multiple constellations, and then detect faulty satellites in the follow-up ambiguity search and position estimation processes. The concept of Virtual Galileo Constellation (VGC) is used to generate useful data sets of dual-constellations for performance analysis. Experimental results from a 24-h data set demonstrate that with GPS&VGC constellations, McRAIM can significantly enhance the detection and exclusion probabilities of two simultaneous faulty satellites in RTK solutions.

The structure of the copper(II) complex with 4,5-dichlorophthalic acid: The 1:1 complex 'adduct' salt trans-tetraaqua(2-carboxy-4,5-dichlorobenzoato)(4,5-dichlorobenzene-1,2-dicarboxylic acid)copper(II) 2-carboxy-4,5-dichlorobenzoate

The unusual (1:1) complex ‘adduct’ salt of copper(II) with 4,5-dichlorophthalic acid (H2DCPA), having formula [Cu(H2O)4(C8H3Cl2O4) (C8H4Cl2O4)] . (C8H3Cl2O4) has been synthesized and characterized using single-crystal X-ray diffraction. Crystals are monoclinic, space group P21/c, with Z = 4 in a cell with dimensions a = 20.1376(7), b =12.8408(4) c = 12.1910(4) Å, β = 105.509(4)o. The complex is based on discrete tetragonally distorted octahedral [CuO6] coordination centres with the four water ligands occupying the square planar sites [Cu-O, 1.962(4)-1.987(4) Å] and the monodentate carboxyl-O donors of two DCPA ligand species in the axial sites. The first of these bonds [Cu-O, 2.341(4) Å] is with an oxygen of a HDCPA monoanion, the second with an oxygen of a H2DCPA acid species [Cu-O, 2.418(4) Å]. The un-coordinated ‘adduct’ molecule is a HDCPA counter anion which is strongly hydrogen-bonded to the coordinated H2DCPA ligand [O… O, 2.503(6) Å] while a number of peripheral intra- and intermolecular hydrogen-bonding interactions give a two-dimensional network structure.

Optical absorption, infrared, Raman and EPR spectral studies on natural Iowaite mineral

Natural iowaite, magnesium–ferric oxychloride mineral having light green color originating from Australia has been characterized by EPR, optical, IR, and Raman spectroscopy. The optical spectrum exhibits a number of electronic bands due to both Fe(III) and Mn(II) ions in iowaite. From EPR studies, the g values are calculated for Fe(III) and g and A values for Mn(II). EPR and optical absorption studies confirm that Fe(III) and Mn(II) are in distorted octahedral geometry. The bands that appear both in NIR and Raman spectra are due to the overtones and combinations of water and carbonate molecules. Thus EPR, optical, and Raman spectroscopy have proven most useful for the study of the chemistry of natural iowaite and chemical changes in the mineral.

Identifying work related injuries : comparison of methods for interrogating text fields

Background: Work-related injuries in Australia are estimated to cost around $57.5 billion annually, however there are currently insufficient surveillance data available to support an evidence-based public health response. Emergency departments (ED) in Australia are a potential source of information on work-related injuries though most ED’s do not have an ‘Activity Code’ to identify work-related cases with information about the presenting problem recorded in a short free text field. This study compared methods for interrogating text fields for identifying work-related injuries presenting at emergency departments to inform approaches to surveillance of work-related injury.---------- Methods: Three approaches were used to interrogate an injury description text field to classify cases as work-related: keyword search, index search, and content analytic text mining. Sensitivity and specificity were examined by comparing cases flagged by each approach to cases coded with an Activity code during triage. Methods to improve the sensitivity and/or specificity of each approach were explored by adjusting the classification techniques within each broad approach.---------- Results: The basic keyword search detected 58% of cases (Specificity 0.99), an index search detected 62% of cases (Specificity 0.87), and the content analytic text mining (using adjusted probabilities) approach detected 77% of cases (Specificity 0.95).---------- Conclusions The findings of this study provide strong support for continued development of text searching methods to obtain information from routine emergency department data, to improve the capacity for comprehensive injury surveillance.

Micro-family-owned business : transitory survival mechanism or long term wealth creator?

Principal Topic: Entrepreneurship is key to employment, innovation and growth (Acs & Mueller, 2008), and as such, has been the subject of tremendous research in both the economic and management literatures since Solow (1957), Schumpeter (1934, 1943), and Penrose (1959). The presence of entrepreneurs in the economy is a key factor in the success or failure of countries to grow (Audretsch and Thurik, 2001; Dejardin, 2001). Further studies focus on the conditions of existence of entrepreneurship, influential factors invoked are historical, cultural, social, institutional, or purely economic (North, 1997; Thurik 1996 & 1999). Of particular interest, beyond the reasons behind the existence of entrepreneurship, are entrepreneurial survival and good ''performance'' factors. Using cross-country firm data analysis, La Porta & Schleifer (2008) confirm that informal micro-businesses provide on average half of all economic activity in developing countries. They find that these are utterly unproductive compared to formal firms, and conclude that the informal sector serves as a social security net ''keep[ing] millions of people alive, but disappearing over time'' (abstract). Robison (1986), Hill (1996, 1997) posit that the Indonesian government under Suharto always pointed to the lack of indigenous entrepreneurship , thereby motivating the nationalisation of all industries. Furthermore, the same literature also points to the fact that small businesses were mostly left out of development programmes because they were supposed less productive and having less productivity potential than larger ones. Vial (2008) challenges this view and shows that small firms represent about 70% of firms, 12% of total output, but contribute to 25% of total factor productivity growth on average over the period 1975-94 in the industrial sector (Table 10, p.316). ---------- Methodology/Key Propositions: A review of the empirical literature points at several under-researched questions. Firstly, we assess whether there is, evidence of small family-business entrepreneurship in Indonesia. Secondly, we examine and present the characteristics of these enterprises, along with the size of the sector, and its dynamics. Thirdly, we study whether these enterprises underperform compared to the larger scale industrial sector, as it is suggested in the literature. We reconsider performance measurements for micro-family owned businesses. We suggest that, beside productivity measures, performance could be appraised by both the survival probability of the firm, and by the amount of household assets formation. We compare micro-family-owned and larger industrial firms' survival probabilities after the 1997 crisis, their capital productivity, then compare household assets of families involved in business with those who do not. Finally, we examine human and social capital as moderators of enterprises' performance. In particular, we assess whether a higher level of education and community participation have an effect on the likelihood of running a family business, and whether it has an impact on households' assets level. We use the IFLS database compiled and published by RAND Corporation. The data is a rich community, households, and individuals panel dataset in four waves: 1993, 1997, 2000, 2007. We now focus on the waves 1997 and 2000 in order to investigate entrepreneurship behaviours in turbulent times, i.e. the 1997 Asian crisis. We use aggregate individual data, and focus on households data in order to study micro-family-owned businesses. IFLS data covers roughly 7,600 households in 1997 and over 10,000 households in 2000, with about 95% of 1997 households re-interviewed in 2000. Households were interviewed in 13 of the 27 provinces as defined before 2001. Those 13 provinces were targeted because accounting for 83% of the population. A full description of the data is provided in Frankenberg and Thomas (2000), and Strauss et alii (2004). We deflate all monetary values in Rupiah with the World Development Indicators Consumer Price Index base 100 in 2000. ---------- Results and Implications: We find that in Indonesia, entrepreneurship is widespread and two thirds of households hold one or several family businesses. In rural areas, in 2000, 75% of households run one or several businesses. The proportion of households holding both a farm and a non farm business is higher in rural areas, underlining the reliance of rural households on self-employment, especially after the crisis. Those businesses come in various sizes from very small to larger ones. The median business production value represents less than the annual national minimum wage. Figures show that at least 75% of farm businesses produce less than the annual minimum wage, with non farm businesses being more numerous to produce the minimum wage. However, this is only one part of the story, as production is not the only ''output'' or effect of the business. We show that the survival rate of those businesses ranks between 70 and 82% after the 1997 crisis, which contrasts with the 67% survival rate for the formal industrial sector (Ter Wengel & Rodriguez, 2006). Micro Family Owned Businesses might be relatively small in terms of production, they also provide stability in times of crisis. For those businesses that provide business assets figures, we show that capital productivity is fairly high, with rates that are ten times higher for non farm businesses. Results show that households running a business have larger family assets, and households are better off in urban areas. We run a panel logit model in order to test the effect of human and social capital on the existence of businesses among households. We find that non farm businesses are more likely to appear in households with higher human and social capital situated in urban areas. Farm businesses are more likely to appear in lower human capital and rural contexts, while still being supported by community participation. The estimation of our panel data model confirm that households are more likely to have higher family assets if situated in urban area, the higher the education level, the larger the assets, and running a business increase the likelihood of having larger assets. This is especially true for non farm businesses that have a clearly larger and more significant effect on assets than farm businesses. Finally, social capital in the form of community participation also has a positive effect on assets. Those results confirm the existence of a strong entrepreneurship culture among Indonesian households. Investigating survival rates also shows that those businesses are quite stable, even in the face of a violent crisis such as the 1997 one, and as a result, can provide a safety net. Finally, considering household assets - the returns of business to the household, rather than profit or productivity - the returns of business to itself, shows that households running a business are better off. While we demonstrate that uman and social capital are key to business existence, survival and performance, those results open avenues for further research regarding the factors that could hamper growth of those businesses in terms of output and employment.

Modelling Spikes in Electricity Prices

uring periods of market stress, electricity prices can rise dramatically. Electricity retailers cannot pass these extreme prices on to customers because of retail price regulation. Improved prediction of these price spikes therefore is important for risk management. This paper builds a time-varying-probability Markov-switching model of Queensland electricity prices, aimed particularly at forecasting price spikes. Variables capturing demand and weather patterns are used to drive the transition probabilities. Unlike traditional Markov-switching models that assume normality of the prices in each state, the model presented here uses a generalised beta distribution to allow for the skewness in the distribution of electricity prices during high-price episodes.

Raman microscopy of haidingerite Ca(AsO3OH)•H2O and brassite Mg(AsO3OH)•4H2O

Raman spectroscopy has been used to study the arsenate minerals haidingerite Ca(AsO3OH)•H2O and brassite Mg(AsO3OH)•4H2O. Intense Raman bands in haidingerite spectrum observed at 745 and 855 cm-1 are assigned to the (AsO3OH)2- ν3 antisymmetric stretching and ν1 symmetric stretching vibrational modes. For brassite two similarly assigned intense bands are found at 809 and 862 cm-1. The observation of multiple Raman bands in the (AsO3OH)2- stretching and bending regions suggests that the arsenate tetrahedrons in the crystal structures of both minerals studied are strongly distorted. Broad Raman bands observed at 2842 cm-1 for haidingerite and 3035 cm-1 for brassite indicate strong hydrogen bonding of water molecules in the structure of these minerals. OH…O hydrogen bond lengths were calculated from the Raman spectra based on empiric relations.

EPR and optical absorption spectral studies on voglite mineral

A voglite mineral sample of Volrite Canyon #1 mine, Frey Point, White Canyon Mine District, San Juan County, Utah, USA is used in the present study. An EPR study on powdered sample confirms the presence of Mn(II) and Cu(II). Optical absorption spectral results are due to Cu(II) which is in distorted octahedron. NIR results are indicating the presence of water fundamentals.

Structure of selected basic copper (II) sulphate minerals based upon spectroscopy : implications for hydrogen bonding

The application of near-infrared and infrared spectroscopy has been used for identification and distinction of basic Cu-sulphates that include devilline, chalcoalumite and caledonite. Near-infrared spectra of copper sulphate minerals confirm copper in divalent state. Jahn-Teller effect is more significant in chalcoalumite where 2B1g ® 2B2g transition band shows a larger splitting (490 cm-1) confirming more distorted octahedral coordination of Cu2+ ion. One symmetrical band at 5145 cm-1 with shoulder band 5715 cm-1 result from the absorbed molecular water in the copper complexes are the combinations of OH vibrations of H2O. One sharp band at around 3400 cm-1 in IR common to the three complexes is evidenced by Cu-OH vibrations. The strong absorptions observed at 1685 and 1620 cm-1 for water bending modes in two species confirm strong hydrogen bonding in devilline and chalcoalumite. The multiple bands in v3 and v4(SO4)2- stretching regions are attributed to the reduction of symmetry to the sulphate ion from Td to C2V. Chalcoalumite, the excellent IR absorber over the range 3800-500 cm-1 is treated as most efficient heat insulator among the Cu-sulphate complexes.

An application of near-infrared and mid-infrared spectroscopy to the study of natural halotrichites : halotrichite, apjohnite and wupatkiite

Near-infrared (NIR) and Fourier transform infrared (FTIR) spectroscopy have been used to determine the mineralogical character of isomorphic substitutions for Mg2+ by divalent transition metals Fe, Mn, Co and Ni in natural halotrichite series. The minerals are characterised by d-d transitions in NIR region 12000-7500 cm-1. NIR spectrum of halotrichite reveals broad feature from 12000 to 7500 cm-1 with a splitting of two bands resulting from ferrous ion transition 5T2g ® 5Eg. The presence of overtones of OH- fundamentals near 7000 cm-1 confirms molecular water in the mineral structure of the halotrichite series. The appearance of the most intense peak at around 5132 cm-1 is a common feature in the three minerals and is derived from combination of OH- vibrations of water molecules and 2 water bending modes. The influence of cations like Mg2+, Fe2+, Mn2+, Co2+, Ni2+ shows on the spectra of halotrichites. Especially wupatkiite-OH stretching vibrations in which bands are distorted conspicuously to low wave numbers at 3270, 2904 and 2454 cm-1. The observation of high frequency 2 mode in the infrared spectrum at 1640 cm-1 indicates coordination of water molecules is strongly hydrogen bonded in natural halotrichites. The splittings of bands in 3 and 4 (SO4)2- stretching regions may be attributed to the reduction of symmetry from Td to C2v for sulphate ion. This work has shown the usefulness of NIR spectroscopy for the rapid identification and classification of the halotrichite minerals.

Optimising figure of merit for phonetic spoken term detection

This paper introduces a novel technique to directly optimise the Figure of Merit (FOM) for phonetic spoken term detection. The FOM is a popular measure of sTD accuracy, making it an ideal candiate for use as an objective function. A simple linear model is introduced to transform the phone log-posterior probabilities output by a phe classifier to produce enhanced log-posterior features that are more suitable for the STD task. Direct optimisation of the FOM is then performed by training the parameters of this model using a non-linear gradient descent algorithm. Substantial FOM improvements of 11% relative are achieved on held-out evaluation data, demonstrating the generalisability of the approach.