Physics-based prognostic modelling of filter clogging phenomena

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 system are the unfortunate outcomes due 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 lives of 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

A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction

Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition

Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

Prognostics with autoregressive moving average for railway turnouts

Turnout systems are one of the most critical systems on railway infrastructure. Diagnostics and prognostics on turnout system have ability to increase the reliability & availability and reduce the downtime of the railway infrastructure. Even though diagnostics on railway turnout systems have been reported in the literature, reported studies on prognostics in railway turnout system is very sparse. This paper presents autoregressive moving average model based prognostics on railway turnouts. The model is applied to data collected from real turnout systems. The failure progression is obtained manually using the exponential degradation model. Remaining Useful Life of ten turnout systems have been reported and results are very promising

Physics-based Degradation Modelling for Filter Clogging

Separation of solids from fluid is a vital process to achieve the desired level of purification in industry. Contaminant filtration is a common process in a variety of applications in industry. Clogging of filter phenomena is the primary failure mode leading to replacement or cleansing of filter. Reduced performance and efficiency or cascading failures are the unfortunate outcomes of a clogged filter. For instance, solid contaminants in fuel may lead to performance reduction in the engine and rapid wear in the fuel pump. This paper presents the development of an experimental rig to collect accelerated filter clogging data and a physics-based degradation model to represent the filter clogging. In the experimental rig, pressure drop across the filter, flow rate, and filter mesh images are acquired during the accelerated clogging experiments. The pressure drop across the filter due to deposition of suspended solids in the liquid is modelled and employed in the degradation modelling. Then, the physics based degradation model simulated using MatLab is compared with the real clogging data and the effectiveness of the degradation model is evaluated

Maintenance planning with prognostics for systems located in various places

Predictive maintenance has been attracting researchers and industry in recent years, since maintenance and repair of assets is one of the most contributing factors of operating & support cost. Predictive maintenance proposes to maintain the assets only when necessary aiming to reduce the unnecessary repair and maintenance by monitoring the health of the assets. The expected time of the failure is estimated by analyzing the monitored signals and remaining useful life of the asset before failure is used to plan, get prepared and perform the maintenance. When one team is responsible for maintenance of systems that are located in various places, the travel time between these systems should also be incorporated in the maintenance planning. Off shore wind farms and railway switches are two examples of these systems. This paper presents formulation of the problem that incorporates travel times between systems and prognostics information obtained from each system.This research was supported by the The Scientific and Technological Research Council of Turkey (TUBITAK) under Project 108M275