Feature extraction and evaluation for Health Assessment and Failure prognostics.

International audienceThe estimation of Remaining Useful Life (RUL) of industrial equipments can be realized on their most critical components. Based on this assumption, the identified critical component must be monitored to track its health state during its operation. Then, the acquired data are processed to extract relevant features, which are used for RUL estimation. This paper presents an evaluation method for the goodness of the features, extracted from raw monitoring signals, for health assessment and prognostics of critical industrial components. The evaluation method is applied to several simulated datasets as well as features obtained from a particular application on bearings

Integrated maintenance and mission planning using remaining useful life information

The modern world requires high reliability and availability with minimum ownership cost for complex industrial systems (high-value assets). Maintenance and mission planning are two major interrelated tasks affecting availability and ownership cost. Both tasks play critical roles in cost savings and effective utilization of the assets, and cannot be performed without taking each other into consideration. Maintenance schedule may make an asset unavailable or too risky to use for a mission. Mission type and duration affect the health of the system, which affects the maintenance schedule. This article presents a mathematical formulation for integrated maintenance and mission planning for a fleet of high-value assets, using their current and forecast health information. An illustrative example for a fleet of unmanned aerial vehicles is demonstrated and evolutionary-based solutions are presented

Segmentation Based Feature Evaluation and Fusion for Prognostics

Quantification of feature goodness, called feature evaluation, is crucial in the identification of best features and achieving high accuracy in diagnostics and prognostics. Even though feature evaluation for diagnostics is a mature area, it is a developing research area for prognostics. The feature goodness for prognostics is measured by change in degradation. Most, if not all, of existing methods, analyze the feature change in the whole failure degradation. In other words, features collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be a good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents a methodology that divides the features into segments, each of which may have different goodness for prognostics. Thus, some part of the feature may be good, whereas the others not. The presented approach leads to extract more value from the features’ changing properties during the failure degradation. The method has been applied to simulated and real datasets obtained from Li-ion batteries aging tests. State of health (SoH) estimation accuracy is enhanced with the presented approach

A new hybrid prognostic methodology

Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available

Degradation-level assessment and online prognostics for sliding chair failure on point machines

This paper presents a degradation-level assessment and failure prognostics methodology for degrading systems. The proposed methodology consists of offline and online phases. In the offline phase, different time-domain health indicators (HIs) are extracted and the best indicator of degradation is selected by filter-based methods. Then, a degradation model is defined and its parameters are estimated using the selected HI. In the online phase, the k-means clustering is utilized to detect a change(s) in the system’s health state and to trigger failure prognostics for remaining useful life (RUL) prediction. The degradation model parameters are updated as new data are available, and the RUL is predicted iteratively. The proposed methodology is implemented on point machine sliding chair degradation using in-field condition monitoring (CM) data. The results show that the methodology can be effectively used in machine degradation-level assessment and in online RUL predictions

Railway point machine prognostics based on feature fusion and health state assessment

This paper presents a condition monitoring approach for point machine prognostics to increase the reliability, availability, and safety in railway transportation industry. The proposed approach is composed of three steps: 1) health indicator (HI) construction by data fusion, 2) health state assessment, and 3) failure prognostics. In Step 1, the time-domain features are extracted and evaluated by hybrid and consistency feature evaluation metrics to select the best class of prognostics features. Then, the selected feature class is combined with the adaptive feature fusion algorithm to build a generic point machine HI. In Step 2, health state division is accomplished by time-series segmentation algorithm using the fused HI. Then, fault detection is performed by using a support vector machine classifier. Once the faulty state has been classified (i.e., incipient/starting fault), the single spectral analysis recurrent forecasting is triggered to estimate the component remaining useful life. The proposed methodology is validated on in-field point machine sliding-chair degradation data. The results show that the approach can be effectively used in railway point machine monitoring

Energy and Utilities Infrastructure: Can All be in One?

In today‘s developed society it is fully expected that every household is provided with general utility products such as heating, lighting, water supply, communication, and waste removal. Provision of these utility products requires large and complex physical, economic and social structures that interact and are interdependent. Furthermore, we underline that each distinct utility product (communication, transportation, water, etc.) provided to our households incurs similar material and embodied energy expenses. But are such structures and their respective expenses really necessary? Or could energy (and other resources) be saved by reducing redundant utility infrastructures, while still maintaining services to the households? Conventional approaches to improved utility provision focus on better management models with optimization, enhanced handling, and increased efficiency in organisations. This paper, on the other hand, presents a novel and radical idea to address this complex problem, by moving from the management level to the scientific & technological level. The paper challenges the need for distinct utility infrastructures for household utility products provision. In particular, the paper discusses the emerging scientific and technological options for using a single energy-provision infrastructure, which would potentially deliver the full set of household utility services

A new adaptive prognostics approach based on hybrid feature selection with application to point machine monitoring

This paper proposes a new adaptive prognostics approach consisting of hybrid feature selection and remaining-useful-life (RUL) estimation steps for railway point machines. In step-1, different time-domain based features are extracted and the best ones are selected by the hybrid feature selection method. Then, a degradation model is fitted to each of the selected features and the parameters are estimated. In step-2, the RUL of the component is predicted by using the proposed adaptive prognostics approach. The adaptive prognostics is based on the weighted likelihood combination of the estimated model parameters. The model parameters each of which estimated by curve fitting are used in the calculation of the likelihood probability weights. Then, an adaptive degradation model is built by using the weighted combination of the model parameter estimates and the component RUL is estimated. The proposed approach is validated on in-field point machine sliding-chair degradation and the results are discussed

Characterising Conversion Points and Complex Infrastructure Systems: Creating a System Representation for Agent-Based Modelling

Infrastructure, which is used to extract, transport, store, and transform resources into products or services to meet our utility needs faces numerous challenges caused by the agency of the various actors in the system. To understand these challenges, we propose it is necessary to move beyond considering each utility system as a distinct silo. In this paper, a conversion points approach is developed to characterize multiutility systems at any scale and for any specific or theoretical location. The story is told of the development of a conversion points approach and its application is examined using an agent-based model. Transport, energy, water, waste, and telecommunications systems are governed and run independently but in practice are highly interdependent. A way to represent all utility systems in an integrated way is described and the benefits of this representation are applied to UK household consumers

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising