Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create !! and ! metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

Prognostics Under the Conditions of Limited Failure Data Availability

[Restricted]Engineering and Physical Sciences Research Counci

Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS