Data-Driven Based Fault Prognosis for Industrial Systems:A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs,which is vital for ensuring the stability,safety and long lifetime of degrading industrial systems.According to the results of fault prognosis,the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance.With the increased complexity and the improved automation level of industrial systems,fault prognosis techniques have become more and more indispensable.Particularly,the datadriven based prognosis approaches,which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data,gain great attention from different industrial sectors.In this context,the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems.Firstly,the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.Moreover,based on the different data characteristics that exist in industrial systems,the corresponding fault prognosis methodologies are illustrated,with emphasis on analyses and comparisons of different prognosis methods.Finally,we reveal the current research trends and look forward to the future challenges in this field.This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Feature Extraction Technique for Fault Prognosis Based on Fault Trend Analysis

Fault prognosis is one of the key techniques for prognosis and health management,and an effective fault feature can improve prediction accuracy and performance. A novel approach of feature extraction for fault prognosis based on fault trend analysis was proposed in this paper. In order to describe the ability of tracking fault growth process,definitions and calculations of fault trackability was developed, and the feature which had the maximum fault trackability was selected for fault prognosis. The vibration data in bearing life tests were used to verify the effectiveness of the method was proposed. The results showed that the trackability of energy entropy for bearing fault growth was the maximum,and it was the best fault feature among selected features root mean square( RMS),kurtosis,new moment and energy entropy. The proposed approach can provide a better strategy for fault feature extraction of bearings in order to improve prediction accuracy.

Establishment of a Fault Prognosis Model Using Wavelet Neural Networks and Its Engineering Application

Fault diagnosis is confronted with two problems; how to '' measure'' the growthof a fault and how to predict the remaining useful lifetime of such a failing component or machine.This paper attempts to solve these two problems by proposing a model of fault prognosis usingwavelet basis neural network. Gaussian radial basis functions and Mexican hat wavelet frames areused as scaling functions and wavelets, respectively. The centers of the basis functions arecalculated using a dyadic expansion scheme and a k-means clustering algorithm.

A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis

Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines.Currently in the wind power industry,maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable,instead of being determined by the actual condition of the wind turbine.Among the total maintenance costs,approximately 25%~35%is related to regularly scheduled preventive maintenance and 65%~75%to unscheduled corrective maintenance.To reduce the failure rate and level and maintenance costs and improve the availability,reliability,safety,and lifespans of wind turbines,it is desirable to perform condition-based predictive maintenance for wind turbines,which will require a high-fidelity online prognostic condition monitoring system(CMS)for fault diagnosis and prognosis and remaining useful life(RUL)prediction of wind turbines.Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction.Compared to vibration monitoring,the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost,hardware complexity,implementation,and reliability.This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.