Physics-based prognostic modelling of filter clogging phenomena

Abstract

In industry,contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the systemare the unfortunate outcomesdue to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful livesof the filters used in the experimental rig have been reported in the paper.The results show that the presented methodology provides significantly accurate and precise prognostic results.

Predicting Remaining Useful Life with Similarity-Based Priors

“…Essentially, two types of techniques exist for predicting RUL: model-based and data-driven techniques. Model-based techniques use physical models to accurately represent the wear and tear of a component over time [5]. Data-driven techniques do not presume any knowledge about how a component wears out over time, but merely predicts the RUL based on past observations.…”

“…Third, to avoid the amplification of clear outliers, they are removed with a Hampel filter [14]. Fourth, we focus on the 'exponential deterioration stage' of the filter's life cycle [5], because-according to the company-the start of that stage is early enough to be able to act on time, and because it provides us with a dataset that is suitable for similarity-based RUL prediction techniques. The start and end of the exponential deterioration stage must be derived from data.…”

Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve

“…In general, methods of prognostic and diagnostics can be categorized into physics-based, data-driven, and hybrid approaches [3,4]. The physics-based method uses a specific physical model to represent the normal machine state and detects the potential failure types based on the deviations between the actual system and the physical model [5][6][7][8][9][10][11]. Hashemnia et al [5,6] used frequency response analysis diagnostics to improve the fault detection of power transformer winding.…”

“…Lu et al [8] proposed a physics-based prognostic model for the rolling element bearings using the realized volatility and wavelet neural network to predict their remaining life. Eker et al [9] integrated a physics-based clogging progression model with particle filter to predict the future clogging levels and the remaining useful life of the fuel filters. However, due to the electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging process [12], it is difficult to establish an accurate and reliable physical model for the solenoid valve.…”

Preliminary analysis on an IVHM approach for prognosis of high-pressure filters for hydraulic Power Control Modules of helicopters

“…The development of novel prognostic solutions to optimize the filter maintenance operations would hence provide advantages in terms of cost reduction and system availability. Literature on the subject is so far almost non-existent and limited to experimental studies in laboratory conditions [1,2]. The study of novel PHM techniques for PCMs is further complicated by the lack of localized sensors, which makes unavailable the direct measurement of physical quantities instrumental to the health monitoring analysis.…”