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.

Development of descriptor tools for the characterization of Australian sugar mill evaporator scale

Cleaning of sugar mill evaporators is an expensive exercise. Identifying the scale components assists in determining which chemical cleaning agents would result in effective evaporator cleaning. The current methods (based on x-ray diffraction techniques, ion exchange/high performance liquid chromatography and thermogravimetry/differential thermal analysis) used for scale characterisation are difficult, time consuming and expensive, and cannot be performed in a conventional analytical laboratory or by mill staff. The present study has examined the use of simple descriptor tests for the characterisation of Australian sugar mill evaporator scales. Scale samples were obtained from seven Australian sugar mill evaporators by mechanical means. The appearance, texture and colour of the scale were noted before the samples were characterised using x-ray fluorescence and x-ray powder diffraction to determine the compounds present. A number of commercial analytical test kits were used to determine the phosphate and calcium contents of scale samples. Dissolution experiments were carried out on the scale samples with selected cleaning agents to provide relevant information about the effect the cleaning agents have on different evaporator scales. Results have shown that by simply identifying the colour and the appearance of the scale, the elemental composition and knowing from which effect the scale originates, a prediction of the scale composition can be made. These descriptors and dissolution experiments on scale samples can be used to provide factory staff with an on-site rapid process to predict the most effective chemicals for chemical cleaning of the evaporators.

Fouling of tubular ceramic membranes during processing of cane sugar juice

This paper examines the fouling characteristics of four tubular ceramic membranes with pore sizes 300 kDa, 0.1 μm and 0.45 μm installed in a pilot plant at a sugar factory for processing clarified cane sugar juices. All the membranes, except the one with a pore size of 0.45 μm, generally gave reproducible results through the trials, were easy to clean and could handle operation at high volumetric concentration factors. Analysis of fouled and cleaned ceramic membranes revealed that polysaccharides, lipids and to a lesser extent, polyphenols, as well as other colloidal particles cause fouling of the membranes. Electrostatic and hydrophobic forces cause strong aggregation of the polymeric components with one another and with colloidal particles. To combat irreversible fouling of the membranes, treatment options that result in the removal of particles having a size range of 0.2–0.5 μm and in addition remove polymeric impurities, need to be identified. Chemical and microscopic evaluations of the juices and the structural characterisation of individual particles and aggregates identified options to mitigate the fouling of membranes. These include conditioning the feed prior to membrane filtration to break up the network structure formed between the polymers and particles in the feed and the use of surfactants to prevent the aggregation of polymers and particles.

Green cane impact on sugar processing : ISSCT process workshop 2008

The ISSCT Process Section workshop held in Réunion 20–23 October 2008 was attended by 51 delegates from 10 countries. The theme was Green cane impact on sugar processing. The workshop provided a valuable and timely opportunity to review and discuss the impact on factory operations and performance from a green cane supply that could include significant levels of trash. It was particularly relevant to those mills that were considering options to boost their biomass intake for increased co-generation capacity. Several of the speakers related their experiences with processing ‘whole of crop’ cane supplies through the factory. Speakers detailed the problems and increased losses that were incurred when processing cane with high trash levels. The consensus of the delegates was that the best scenario would involve a cane-cleaning plant at the factory so that only clean cane would be processed through the factory. The forum recommended that more research was required to address the issues of increased impurities in the process streams associated with high trash levels. Site visits to the two factories and a cane-delivery station were arranged as part of the workshop.

Formation of thermodynamically unstable calcium oxalate dihydrate in sugar mill evaporators

Calcium oxalate (CaOX) is the most intractable scale component to remove in sugar mill evaporators by either mechanical or chemical means. The operating conditions of sugar mill evaporators should preferentially favour the formation of the thermodynamically stable calcium oxalate monohydrate (COM), yet analysis of scale deposit from different sugar factories have shown that calcium oxalate dihydrate (COD) is usually the predominant phase, and in some cases is the only hydrate formed. The effects of trans-aconitic, succinic and acetic acids, all of which are present in sugarcane juice, and ethylenediamine tetraacetic acid disodium salt (EDTA) on the growth of CaOX crystals have been examined by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray powder diffraction (XRD) and thermogravimetric analysis (TGA). trans-Aconitic acid, which constitutes two-thirds of the organic acid component in sugarcane juice, in the presence of sugar resulted in the formation of COD and COM in a 3:1 ratio. EDTA was the most effective acid to promote the formation of COD followed by trans-aconitic acid, then acetic acid and lastly succinic acid.

Accumulation of recombinant cellobiohydrolase and endoglucanase in the leaves of mature transgenic sugar cane

A major strategic goal in making ethanol from lignocellulosic biomass a cost-competitive liquid transport fuel is to reduce the cost of production of cellulolytic enzymes that hydrolyse lignocellulosic substrates to fermentable sugars. Current production systems for these enzymes, namely microbes, are not economic. One way to substantially reduce production costs is to express cellulolytic enzymes in plants at levels that are high enough to hydrolyse lignocellulosic biomass. Sugar cane fibre (bagasse) is the most promising lignocellulosic feedstock for conversion to ethanol in the tropics and subtropics. Cellulolytic enzyme production in sugar cane will have a substantial impact on the economics of lignocellulosic ethanol production from bagasse. We therefore generated transgenic sugar cane accumulating three cellulolytic enzymes, fungal cellobiohydrolase I (CBH I), CBH II and bacterial endoglucanase (EG), in leaves using the maize PepC promoter as an alternative to maize Ubi1 for controlling transgene expression. Different subcellular targeting signals were shown to have a substantial impact on the accumulation of these enzymes; the CBHs and EG accumulated to higher levels when fused to a vacuolar-sorting determinant than to an endoplasmic reticulum-retention signal, while EG was produced in the largest amounts when fused to a chloroplast-targeting signal. These results are the first demonstration of the expression and accumulation of recombinant CBH I, CBH II and EG in sugar cane and represent a significant first step towards the optimization of cellulolytic enzyme expression in sugar cane for the economic production of lignocellulosic ethanol.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Development of a diagnostic tool for condition monitoring of rotating machinery

This paper presents an overview of the CRC for Infrastructure and Engineering Asset Management (CIEAM)’s rotating machine health monitoring project and the status of the research progress. The project focuses on the development of a comprehensive diagnostic tool for condition monitoring and systematic analysis of rotating machinery. Particularly attention focuses on the machine health monitoring of diesel engines, compressors and pumps by using acoustic emission and vibration-based monitoring techniques. The paper also provides a brief summary of the work done by the three main research collaborating partners in the project, namely, Queensland University of Technology (QUT), Curtin University of Technology (CUT) and the University of Western Australia (UWA). Preliminary test and analysis results from this work are also reported in the paper