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

Water lubricated bearings

The increasing ecological awareness and stringent requirements for environmental protection have led to the development of water lubricated bearings in many applications where oil was used as the lubricant. The chapter details the theoretical analysis to determine both the static and dynamic characteristics,including the stability (using both the linearised perturbation method and the nonlinear transient analysis) of multiple axial groove water lubricated bearings. Experimental measurements and computational fluid dynamics (CFD) simulations by the Tribology research group at Queensland University of Technology,Australia and Manipal Institute of Technology, India, have highlighted a significant gap in the understanding of the flow phenomena and pressure conditions within the lubricating fluid. An attempt has been made to present a CFD approach to model fluid flow in the bearing with three equi-spaced axial grooves and supplied with water from one end of the bearing. Details of the experimental method used to measure the film pressure in the bearing are outlined. The lubricant is subjected to a velocity induced flow (as the shaft rotates) and a pressure induced flow (as the water is forced from one end of the bearing to the other). Results are presented for the circumferential and axial pressure distribution in the bearing clearance for different loads, speeds and supply pressures. The axial pressure profile along the axial groove located in the loaded part of the bearing is measured. The theoretical analysis shows that smaller the groove angle better will be the load-carrying capacity and stability of these bearings. Results are compared with experimentally measured pressure distributions.

Education on plant and machinery valuation for the real market : Malaysian practicality

The purpose of this paper is to determine and discuss on the plant and machinery valuation syllabus for higher learning education in Malaysia to ensure the practicality of the subject in the real market. There have been limited studies in plant and machinery area, either by scholars or practitioners. Most papers highlighted the methodologies but limited papers discussed on the plant and machinery valuation education. This paper will determine inputs for plant and machinery valuation guidance focussing on the syllabus set up and references for valuers interested in this area of expertise. A qualitative approach via content analysis is conducted to compare international and Malaysian plant and machinery valuation syllabus and suggest improvements for Malaysian syllabus. It is found that there are few higher education institutions in the world that provide plant and machinery valuation courses as part of their property studies syllabus. Further investigation revealed that on the job training is the preferable method for plant and machinery valuation education and based on the valuers experience. The significance of this paper is to increase the level of understanding of plant and machinery valuation criteria and provide suggestions to Malaysian stakeholders with the relevant elements in plant and machinery valuation education syllabus.

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.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.