Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Reliability prediction using the non-parametric explicit hazard model : a case study

Survival probability prediction using covariate-based hazard approach is a known statistical methodology in engineering asset health management. We have previously reported the semi-parametric Explicit Hazard Model (EHM) which incorporates three types of information: population characteristics; condition indicators; and operating environment indicators for hazard prediction. This model assumes the baseline hazard has the form of the Weibull distribution. To avoid this assumption, this paper presents the non-parametric EHM which is a distribution-free covariate-based hazard model. In this paper, an application of the non-parametric EHM is demonstrated via a case study. In this case study, survival probabilities of a set of resistance elements using the non-parametric EHM are compared with the Weibull proportional hazard model and traditional Weibull model. The results show that the non-parametric EHM can effectively predict asset life using the condition indicator, operating environment indicator, and failure history.

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.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA

Altered freshwater inflows have affected circulation, salinity, and water quality patterns of Florida Bay, in turn altering the structure and function of this estuary. Changes in water quality and salinity and associated loss of dense turtle grass and other submerged aquatic vegetation (SAV) in Florida Bay have created a condition in the bay where sediments and nutrients have been regularly disturbed, frequently causing large and dense phytoplankton blooms. These algal and cyanobacterial blooms in turn often cause further loss of more recently established SAV, exacerbating the conditions causing the blooms. Chlorophyll a (CHLA) was selected as an indicator of water quality because it is an indicator of phytoplankton biomass, with concentrations reflecting the integrated effect of many of the water quality factors that may be altered by restoration activities. Overall, we assessed the CHLA indicator as being (1) relevant and reflecting the state of the Florida Bay ecosystem, (2) sensitive to ecosystem drivers (stressors, especially nutrient loading), (3) feasible to monitor, and (4) scientifically defensible. Distinct zones within the bay were defined according to statistical and consensual information. Threshold levels of CHLA for each zone were defined using historical data and scientific consensus. A presentation template of condition of the bay using these thresholds is shown as an example of an outreach product.

Is land condition a useful indicator of soil organic carbon stock in Australia?

The grazing lands of northern Australia contain a substantial soil organic carbon (SOC) stock due to the large land area. Manipulating SOC stocks through grazing management has been presented as an option to offset national greenhouse gas emissions from agriculture and other industries. However, research into the response of SOC stocks to a range of management activities has variously shown positive, negative or negligible change. This uncertainty in predicting change in SOC stocks represents high project risk for government and industry in relation to SOC sequestration programs. In this paper, we seek to address the uncertainty in SOC stock prediction by assessing relationships between SOC stocks and grazing land condition indicators. We reviewed the literature to identify land condition indicators for analysis and tested relationships between identified land condition indicators and SOC stock using data from a paired-site sampling experiment (10 sites). We subsequently collated SOC stock datasets at two scales (quadrat and paddock) from across northern Australia (329 sites) to compare with the findings of the paired-site sampling experiment with the aim of identifying the land condition indicators that had the strongest relationship with SOC stock. The land condition indicators most closely correlated with SOC stocks across datasets and analysis scales were tree basal area, tree canopy cover, ground cover, pasture biomass and the density of perennial grass tussocks. In combination with soil type, these indicators accounted for up to 42% of the variation in the residuals after climate effects were removed. However, we found that responses often interacted with soil type, adding complexity and increasing the uncertainty associated with predicting SOC stock change at any particular location. We recommend that caution be exercised when considering SOC offset projects in northern Australian grazing lands due to the risk of incorrectly predicting changes in SOC stocks with change in land condition indicators and management activities for a particular paddock or property. Despite the uncertainty for generating SOC sequestration income, undertaking management activities to improve land condition is likely to have desirable complementary benefits such as improving productivity and profitability as well as reducing adverse environmental impact.

Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

A comparison of spatially explicit landscape representation methods and their relationship to stream condition

1. Biodiversity, water quality and ecosystem processes in streams are known to be influenced by the terrestrial landscape over a range of spatial and temporal scales. Lumped attributes (i.e. per cent land use) are often used to characterise the condition of the catchment; however, they are not spatially explicit and do not account for the disproportionate influence of land located near the stream or connected by overland flow. 2. We compared seven landscape representation metrics to determine whether accounting for the spatial proximity and hydrological effects of land use can be used to account for additional variability in indicators of stream ecosystem health. The landscape metrics included the following: a lumped metric, four inverse-distance-weighted (IDW) metrics based on distance to the stream or survey site and two modified IDW metrics that also accounted for the level of hydrologic activity (HA-IDW). Ecosystem health data were obtained from the Ecological Health Monitoring Programme in Southeast Queensland, Australia and included measures of fish, invertebrates, physicochemistry and nutrients collected during two seasons over 4 years. Linear models were fitted to the stream indicators and landscape metrics, by season, and compared using an information-theoretic approach. 3. Although no single metric was most suitable for modelling all stream indicators, lumped metrics rarely performed as well as other metric types. Metrics based on proximity to the stream (IDW and HA-IDW) were more suitable for modelling fish indicators, while the HA-IDW metric based on proximity to the survey site generally outperformed others for invertebrates, irrespective of season. There was consistent support for metrics based on proximity to the survey site (IDW or HA-IDW) for all physicochemical indicators during the dry season, while a HA-IDW metric based on proximity to the stream was suitable for five of the six physicochemical indicators in the post-wet season. Only one nutrient indicator was tested and results showed that catchment area had a significant effect on the relationship between land use metrics and algal stable isotope ratios in both seasons. 4. Spatially explicit methods of landscape representation can clearly improve the predictive ability of many empirical models currently used to study the relationship between landscape, habitat and stream condition. A comparison of different metrics may provide clues about causal pathways and mechanistic processes behind correlative relationships and could be used to target restoration efforts strategically.

Experimental investigations of improved dielectric condition monitoring methods on transformer oil-paper insulation system

Frequency Domain Spectroscopy (FDS) is successfully being used to assess the insulation condition of oil filled power transformers. However, it has to date only been implemented on de-energized transformers, which requires the transformers to be shut down for an extended period which can result in significant costs. To solve this issue, a method of implementing FDS under energized condition is proposed here. A chirp excitation waveform is used to replace the conventional sinusoidal waveform to reduce the measurement time in this method. Investigation of the dielectric response under the influence of a high voltage stress at power frequency is reported based on experimental results. To further understand the insulation ageing process, the geometric capacitance effect is removed to enhance the detection of the ageing signature. This enhancement enables the imaginary part of admittance to be used as a new indicator to assess the ageing status of the insulation.

Support for Integrated Ecosystem Assessments of NOAA’s National Estuarine Research Reserves System (NERRS), Volume II: Assessment of Ecological Condition and Stressor Impacts in Subtidal Waters of the North Carolina NERRS

A study was conducted to assess the status of ecological condition and potential human-health risks in subtidal estuarine waters throughout the North Carolina National Estuarine Research Reserve System (NERRS) (Currituck Sound, Rachel Carson, Masonboro Island, and Zeke’s Island). Field work was conducted in September 2006 and incorporated multiple indicators of ecosystem condition including measures of water quality (dissolved oxygen, salinity, temperature, pH, nutrients and chlorophyll, suspended solids), sediment quality (granulometry, organic matter content, chemical contaminant concentrations), biological condition (diversity and abundances of benthic fauna, fish contaminant levels and pathologies), and human dimensions (fish-tissue contaminant levels relative to human-health consumption limits, various aesthetic properties). A probabilistic sampling design permitted statistical estimation of the spatial extent of degraded versus non-degraded condition across these estuaries relative to specified threshold levels of the various indicators (where possible). With some exceptions, the status of these reserves appeared to be in relatively good to fair ecological condition overall, with the majority of the area (about 54%) having various water quality, sediment quality, and biological (benthic) condition indicators rated in the healthy to intermediate range of corresponding guideline thresholds. Only three stations, representing 10.5% of the area, had one or more of these indicators rated as poor/degraded in all three categories. While such a conclusion is encouraging from a coastal management perspective, it should be viewed with some caution. For example, although co-occurrences of adverse biological and abiotic environmental conditions were limited, at least one indicator of ecological condition rated in the poor/degraded range was observed over a broader area (35.5%) represented by 11 of the 30 stations sampled. In addition, the fish-tissue contaminant data were not included in these overall spatial estimates; however, the majority of samples (77% of fish that were analyzed, from 79%, of stations where fish were caught) contained inorganic arsenic above the consumption limits for human cancer risks, though most likely derived from natural sources. Similarly, aesthetic indicators are not reflected in these spatial estimates of ecological condition, though there was evidence of noxious odors in sediments at many of the stations. Such symptoms reflect a growing realization that North Carolina estuaries are under multiple pressures from a variety of natural and human influences. These data also suggest that, while the current status of overall ecological condition appears to be good to fair, long-term monitoring is warranted to track potential changes in the future. This study establishes an important baseline of overall ecological condition within NC NERRS that can be used to evaluate any such future changes and to trigger appropriate management actions in this rapidly evolving coastal environment. (PDF contains 76 pages)

Metallothionein as an indicator of water quality: assessment of the bioavailability of cadmium, copper, mercury and zinc in aquatic animals at the cellular level

The study of metallothioneins (MTs) has greatly improved our understanding of body burdens, metal storage and detoxification in aquatic organisms subjected to contamination by the toxic heavy metals, Cd, Cu, Hg and Zn. These studies have shown that in certain organisms MT status can be used to assess impact of these metals at the cellular level and, whilst validation is currently limited to a few examples, this stress response may be linked to higher levels of organisation, thus indicating its potential for environmental quality assessment. Molluscs, such as Mytilus spp., and several commonly occurring teleost species, are the most promising of the indicator species tested. Natural variability of MT levels caused by the organism's size, condition, age, position in the sexual cycle, temperature and various stressors, can lead to difficulties in interpretation of field data as a definitive response-indicator of metal contamination unless a critical appraisal of these variables is available. From laboratory and field studies these data are almost complete for teleost fish. Whilst for molluscs much of this information is lacking, when suitable controls are utilised and MT measurements are combined with observations of metal partitioning, current studies indicate that they are nevertheless a powerful tool in the interpretation of impact, and may prove useful in water quality assessment.

Evaluation of bioelectrical impedance analysis and Fulton’s condition factor as nonlethal techniques for estimating short-term responses in postsmolt Atlantic salmon (Salmo salar) to food availabili

We evaluated measures of bioelectrical impedance analysis (BIA) and Fulton’s condition factor (K) as potential nonlethal indices for detecting short-term changes in nutritional condition of postsmolt Atlantic salmon (Salmo salar). Fish reared in the laboratory for 27 days were fed, fasted, or fasted and then refed. Growth rates and proximate body composition (protein, fat, water) were measured in each fish to evaluate nutritional status and condition. Growth rates of fish responded rapidly to the absence or reintroduction of food, whereas body composition (% wet weight) remained relatively stable owing to isometric growth in fed fish and little loss of body constituents in fasted fish, resulting in nonsignificant differences in body composition among feeding treatments. The utility of BIA and Fulton’s K as condition indices requires differences in body composition. In our study, BIA measures were not significantly different among the three feeding treatments, and only on the final day of sampling was K of fasted vs. fed fish significantly different. BIA measures were correlated with body composition content; however, wet weight was a better predictor of body composition on both a content and concentration (% wet weight) basis. Because fish were growing isometrically, neither BIA nor K was well correlated with growth rate. For immature fish, where growth rate, rather than energy reserves, is a more important indicator of fish condition, a nonlethal index that reflects shortterm changes in growth rate or the potential for growth would be more suitable as a condition index than either BIA measures or Fulton�

Relative pleopod length as an indicator of size at sexual maturity in slipper (Scyllarides squammosus) and spiny Hawaiian (Panulirus marginatus) lobsters

Body size at gonadal maturity is described for females of the slipper lobster (Scyllarides squammosus) (Scyllaridae) and the endemic Hawaiian spiny lobster (Panulirus marginatus) (Palinuridae) based on microscopic examination of histological preparations of ovaries. These data are used to validate several morphological metrics (relative exopodite length, ovigerous condition) of functional sexual maturity. Relative exopodite length (“pleopod length”) produced consistent estimates of size at maturity when evaluated with a newly derived statistical application for estimating size at the morphometric maturation point (MMP) for the population, identified as the midpoint of a sigmoid function spanning the estimated boundaries of overlap between the largest immature and smallest adult animals. Estimates of the MMP were related to matched (same-year) characterizations of sexual maturity based on ovigerous condition — a more conventional measure of functional maturity previously used to characterize maturity for the two lobster species. Both measures of functional maturity were similar for the respective species and were within 5% and 2% of one another for slipper and spiny lobster, respectively. The precision observed for two shipboard collection series of pleopod-length data indicated that the method is reliable and not dependent on specialized expertise. Precision of maturity estimates for S. squammosus with the pleopod-length metric was similar to that for P. marginatus with any of the other measures (including conventional evidence of ovigerous condition) and greatly exceeded the precision of estimates for S. squammosus based on ovigerous condition alone. The two measures of functional maturity averaged within 8% of the estimated size at gonadal maturity for the respective species. Appendage-to-body size proportions, such as the pleopod length metric, hold great promise, particularly for species of slipper lobsters like S. squammosus for which there exist no other reliable conventional morphological measures of sexual maturity. Morphometric proportions also should be included among the factors evaluated when assessing size at sexual maturity in spiny lobster stocks; previously, these proportions have been obtained routinely only for brachyuran crabs within the Crustacea.