Condition based maintenance optimization using data driven methods

In condition based maintenance (CBM), maintenance activities are scheduled based on the predicted equipment failure times, and the predictions are performed based on conditon monitoirng data, such as vibration and acoustic data. The reported health condition prediction methods can be roughly classified into model-based, and data-driven, and integrated methods. Our research mainly focuses on CBM optimization using data driven methods, such as proportional hazards model (PHM) and artificial neural network (ANN), which don't require equipment physical models. In CBM optimization using PHM, the accuracy of parameter estimation for PHM greatly affects the effectiveness of the optimal maintenance policy. Directly using collected condition monitoring data may iv introduce noise into the CBM optimization, and thus the optimal maintenance policy obtained based on this model may not be really optimal. Therefore, a data processing method, where the actual measurements are fitted first using the Generalized Weibull-FR function, is proposed to remove the external noise before fitting it into the PHM. Effective CBM optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (1) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (2) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available. Therefore, we propose an ANN based CBM optimization approach and a numerical cost evaluation method to address those key challenges. It is observed that the prediction accuracy often improves with the increase of the age of the component. Therefore, we develop a method to quantify the remaining life prediction uncertainty considering the prediction accuracy improvements by modeling the relationship between the mean value as well as standard deviation of prediction error and the life percentage. An effective CBM optimization approach is also proposed to optimize the maintenance schedule. The proposed approaches are demonstrated using some simulated degradation data sets as well as some real-world vibration monitoring data set. They contribute to the general knowledge of CBM, and have the potential to greatly benefit various industries

Desarrollo de un modelo basado en datos a partir de señales de vibración para la detección de fallos en un compresor reciprocante de simple efecto doble etapa

Señala que los compresores recíprocos son máquinas altamente utilizadas en las industrias por ser la principal fuente de aire comprimido. La aplicación de una estrategia de mantenimiento inadecuada para la detección temprana de fallos conduce al incremento de paros inesperados, incluso puede desencadenar eventos catastróficos para los procesos productivos. La detección de fallos en este tipo de máquinas resulta en la mayoría de casos complejo, por la dificultad para monitorear en tiempo real. En los últimos años se ha incrementado el uso de técnicas de modelamiento basado en datos para el diagnóstico de fallos. Estas técnicas requieren de grandes cantidades de datos que no siempre se pueden obtener pues generan altos costos y tiempo excesivo, que son difíciles de solventar desde el punto de vista económico y técnico. El presente trabajo se enfoca en tres aspectos como la adquisición de datos, el desarrollo de un método para el pre-procesamiento de las señales de vibración y por último la propuesta de una metodología para el modelado basado en redes neuronales recurrentes Long Short Term Memory (LSTM) para el diagnóstico de fallos.Tesi

Innovation report : a methodology for estimating gear pump wear-out reliability using pump pressure ripple and an extremely small sample size - the case study of a heavy-duty diesel engine lubrication gear pump

Design for Reliability (DfR) encourages testing products early in the New Product Development (NPD) process to identify and resolve weaknesses quickly. An organisation can then track reliability growth and intervene to ensure the changes in product robustness are in line with a timely release to market. However, for products with long life spans (such as a Heavy-Duty engine (HDE) lubrication gear pump), the evaluation of reliability with an extremely small number of prototype samples is problematic. Budget constraints, product size, and test facilities can limit the possibilities of accurately assessing the initial reliability forming a test planning paradox. The research in this thesis proposes an innovate methodology to minimise this test planning paradox, specific to a gear pump. The method uses step-stress accelerated degradation testing and Bayesian inference to estimate degradation parameters using only a sample size of two. Post-testing, numerical simulation is used to build a degradation model with larger sample sizes and produce a survival distribution at the quantile of interest. Increasing pump outlet pressure above normal usage accelerates the pump wear and pressure ripple measurements are used to monitor the performance degradation. On inspection, the pumps exhibit erosion on the housing and micro pitting of the gear flanks. The innovative use of a Maximal Overlap Discrete Wavelet Transforms (MODWT) with an Autoregressive Moving Average (ARMA 2,1) extracts a feature from the pressure ripple that provides a stochastic, linear and non-monotone degradation path that is appropriately modelled using a Brownian Motion simulation model. Regression analysis provides a drift and diffusion covariate functional relationship to pump outlet pressure. Given the stress-varying environment of an HDE, Monte Carlo simulations overcome the complexity of replicating vehicle drive cycle and produces a credible reliability estimate validated against a similarly designed high mileage pump. The application of this original methodology offers the opportunity to minimise the test planning paradox and satisfies populating the reliability growth chart. It is foreseen the method can be adopted for a wide range of positive displacement pumps where is it possible to measure pressure ripple