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

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

A simple state-based prognostic model for filter clogging

In today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately

Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Engine remaining useful life prediction based on trajectory similarity

The traditional remaining useful life prediction methods need to study the mechanism failure of equipment and the vibration signals can easily be submerged by the noise in the actual operation, in order to solve these problems, the methods of Trajectory similarity based prediction (TSBP) and condition monitoring based on lubricant information are proposed in this paper. The gradient model of lubricant data information which is processed by principal component analysis (PCA) is used to monitor equipment status. Additionally, degradation trajectory abstraction procedure and similarity evaluation procedure are studied in detail. Finally, the both studies are combined for the research of engine remaining useful life prediction and case study proves the simplicity and effectiveness of this method

RUL prediction based on a new similarity-instance based approach.

International audiencePrognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety,reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e. to predict the time after which a component/system will no longer be able to meet its operating requirements. RUL prediction is a challenging task that requires special attention when modeling the prognostics approach. This paper proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter. The method is divided into two steps: an offline and an online step.The purpose of the offline phase is to learn a model that represents the degradation behavior of a critical component using a history of run-to-failure data. This modeling step enablesus to construct a library of health indicators (HI) from run-to-failure data. These HI’s are then used online to estimate the RUL of components at an early stage of life, by comparing their HI’s to the ones of the library built in the offline phase. Our approach makes use of a new similarity measure between HIs. The proposed approach was tested on real turbofan data set and showed good performance compared to other existing approaches

Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.Peer reviewe

Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction

The health state of a rolling bearing keeps changing from a normal state to a slight degradation state followed eventually by a severely degraded state. To make reasonable inspection and maintenance plans, it is necessary to estimate the degradation state and predict the lifetime of a running rolling bearing accurately and in a timely fashion. This paper presents a new method for rolling bearing degradation state estimation and lifetime prediction based on curve similarity recognition. Different from existing methods, this method employs a dynamic time warping algorithm to recognize the curve similarity of those extracted features of rolling bearings in different states of health, which can reflect the intrinsic state of the rolling bearing; it discretizes the bearing degradation state reasonably through curve similarity. Next, the curve similarity is used to train the degradation state estimation model and a support vector machine based lifetime prediction model. Finally, this paper conducts a case study for a rolling bearing with impact degradation and one with wear degradation, respectively. The experimental results indicate that the new proposed method is highly efficient in recognizing the bearing’s degradation state and predicting its lifetime