Predictive maintenance using cox proportional hazard deep learning (original) (raw)
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Survey on Deep Learning applied to predictive maintenance
International Journal of Electrical and Computer Engineering (IJECE), 2020
Prognosis Health Monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each re...
Automobile Predictive Maintenance using Deep Learning
International Journal of Artificial Intelligence and Machine Learning
There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered an...
Neural Computing and Applications
The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is eval...
2023
der the applicability of data-driven predictive maintenance. These obstacles have been regarded in the future works of the presented thesis, which can be briefed as follows: considering the class-label shifts across different working environments; (2) quantifying the uncertainty of the proposed data-driven approaches; (3) realizing explainable deep learning approach that can provide reasons behind the decisions and give descriptions to the end-users.
International Journal of Electrical and Computer Engineering (IJECE), 2022
Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.
Sensors, 2021
Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related l...
Effective Predictive Maintenance to Overcome System Failures—A Machine Learning Approach
2020
As industry is getting advanced day by day incorporating new equipment's on a large scale, there is a need to predict the machine lifetime in order to support the supply chain management. There are many ways of substitutions or upgradations required for any machine over a certain period of time, where its maintenance has become a major challenge. This problem is solved by building an effective predictive maintenance system which provides an intense spotlight for all types of machine industries. The log data is collected from the daily system activity from machines through deployment of various sensors facilitated to monitor the current state of equipment. A huge volume of numerical log data set is analyzed by the system for preparing the time series data for training and analyzing the model. Further steps involve bypassing the anomalies and fetching the clean data. The model is tested focusing on restoration time of any machine. This paper identifies and predicts failures of heavy machines, thus facilitating the predictive maintenance scenario for effective working of the machine at all situations. This work is implemented by LSTM network model for gaining authentic results with numeric data which facilitates major cost savings and offers higher maintenance predictability rate.
IEEE Access
In the era of industry 4.0, safety, efficiency and reliability of industrial machinery is an elementary concern in trade sectors. The accurate remaining useful life (RUL) prediction of an equipment in due time allows us to effectively plan the maintenance operation and mitigate the downtime to raise the revenue of business. In the past decade, data driven based RUL prognostic methods had gained a lot of interest among the researchers. There exist various deep learning-based techniques which have been used for accurate RUL estimation. One of the widely used technique in this regard is the long short-term memory (LSTM) networks. To further improve the prediction accuracy of LSTM networks, this paper proposes a model in which effective pre-processing steps are combined with LSTM network. C-MAPSS turbofan engine degradation dataset released by NASA is used to validate the performance of the proposed model. One important factor in RUL predictions is to determine the starting point of the engine degradation. This work proposes an improved piecewise linear degradation model to determine the starting point of deterioration and assign the RUL target labels. The sensors data is pre-processed using the correlation analysis to choose only those sensors measurement which have a monotonous behavior with RUL, which is then filtered through a moving median filter. The updated RUL labels from the degradation model together with the pre-processed data are used to train a deep LSTM network. The deep neural network when combined with dimensionality reduction and piece-wise linear RUL function algorithms achieves improved performance on aircraft turbofan engine sensor dataset. We have tested our proposed model on all four sub-datasets in C-MAPSS and the results are then compared with the existing methods which utilizes the same dataset in their experimental work. It is concluded that our model yields improvement in RUL prediction and attains minimum root mean squared error and score function values. INDEX TERMS Deep learning, long short-term memory networks, remaining useful life, turbofan engine. This technique focuses on forecasting the error by model-39 ing the degradation trends between input sensors and time-40 to-failure duration of the machine. So, the benefits of this 41 maintenance strategy is that we can eliminate unplanned 42 downtime, reduced maintenance costs and maximize the 43 machine lifetime for safety critical circumstances. One such 44 example is aircraft engine which requires continuous mon-45 itoring of the engine performance. The fault diagnostics 46 and prognostics of aircraft engine has gained great attention 47 over the last few decades [4], [5], [6], [7]. One important 48 component in aircraft engine maintenance is to accurately 49 determine its remaining useful life (RUL) for reducing the 50 maintenance costs while attaining the reliability [8], [9]. RUL 51 prediction model is developed based upon the degradation 52 trends among the various condition monitoring sensors. This 53 model helps in development of maintenance strategy in a 54 targeted manner to eliminate unplanned downtime and maxi-55 mize machine lifetime for safety critical circumstances. Early 56 anomaly detection and timely warning of a failure is vital for 57 maximum utilization of the system. There are basically three 58 types of prognostics techniques used for estimating RUL, 59 physical model-based approaches [10], [11], data-driven 60 approaches [12], [13] and hybrid approaches [14]. 61 Model based approach initially required a comprehensive 62 understanding of the physical architecture of the machine and 63 then applying the laws of physics to obtain the mathematical 64 model of the machine for RUL estimation [15]. Mathemat-65 ical models often take some simplifying assumptions with 66 uncertainty management for a complex industrial machinery, 67 which can impose serious limitations on these techniques and 68 hence degrade the RUL prediction accuracy [16]. 69 Data-driven based prognosis approaches use various sta-70 tistical and machine leaning (ML) algorithms to discover the 71 trends or patterns in the underlying sensor data to estimate 72 RUL of the system. These techniques are suitable for com-73 plex industrial machinery and further, it does not require a 74 thorough understanding of a complete engine or the process. 75 Hybrid method combines both the physics and data-driven 76 based model techniques [17]. 77 In the past decade, data-driven based prognostics meth-78 ods have been exploited by many researchers. These mod-79 els estimate the RUL by analyzing the degradation trend 80 and target trajectory of sensor data. Deep learning methods 81 like autoencoder, convolutional neural networks (CNN), long 82 short-term memory (LSTM) networks and their varianta and 83 combinations have achieved a massive success in the fields 84 of computer vision, speech recognition, video segmentation 85 and predictive maintenance [18]. The major drawback of deep 86 learning algorithm is that it requires a large volume of data 87 for offline training and in the field of prognostics, it is very 88 157 techniques usually employ algorithms like Kalman filter 158 (KF), extended Kalman filter (EKF) and particles filters to 159 come up with mathematical formulation of machine based on 160 multi sensor time series sequence data [26], [27], [28]. Clas-161 sical degradation method such as Eyring model or Weibull 162 distribution was implemented in [29]. Salahshoor et al. [30] 163 used a unified framework of EKF based design for sen-164 sor data fusion algorithm to further enhanced the detec-165 tion and diagnosis of degradation trends and system faults. 166 Ordonez et al. [31] implemented the auto-regressive inte-167 grated moving average (ARIMA) model and support vector 168 machine (SVR) methods collectively to estimate the RUL. 169 The desired features can be created by analyzing prior learn-170 ing about the degradation models as presented in [32]. In [33], 171 it is suggested that failure thresholds or degradation state esti-172 mation is no longer required in learning-oriented approach. 173 Khelif et al. [33] presented machine learning based support 174 vector regression (SVR) model to project the direct associa-175 tion between multivariate sensor data or health index and the 176 aircraft turbofan engine RUL. 177 Across all these techniques for turbofan engine RUL pre-178 diction, deep neural network-based methods have gained vast 179 popularity. Zhang et al. [34] introduced a multi-objective 180 evolutionary algorithm to expand and organized the deep 181 belief network into multiple parallel networks simultane-182 ously to accomplish the two convicting objectives i.e. diver-183 sity and accuracy. These networks attained a fine RUL 184 prediction accuracy especially in case of complicated oper-185 ations and in the presence of noise in input data [35], [36]. 186 Saeidi et al. [37], proposed a naive Bayesian classification 187 algorithm to measure the health index for turbofan engine.
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
2018 IEEE International Conference on Big Data (Big Data)
One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in place. Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a prespecified time window. As these two tasks are related, performing them separately is sub-optimal and might results in inconsistent predictions for the same equipment. In order to alleviate these issues, we propose two methods: Deep Weibull model (DW-RNN) and multi-task learning (MTL-RNN). DW-RNN is able to learn the underlying failure dynamics by fitting Weibull distribution parameters using a deep neural network, learned with a survival likelihood, without training directly on each task. While DW-RNN makes an explicit assumption on the data distribution, MTL-RNN exploits the implicit relationship between the longterm RUL and short-term FP tasks to learn the underlying distribution. Additionally, both our methods can leverage the non-failed equipment data for RUL estimation. We demonstrate that our methods consistently outperform baseline RUL methods that can be used for FP while producing consistent results for RUL and FP. We also show that our methods perform at par with baselines trained on the objectives optimized for either of the two tasks.
IFAC-PapersOnLine, 2020
Aircraft fault detection and prediction is a critical element of preventing failures, reducing maintenance costs, and increasing fleet availability. This paper considers a problem of rare failure prediction in the context of aircraft predictive maintenance. It presents a novel approach of predicting extremely rare failures, based on combining two deep learning techniques, auto-encoder (AE) and Bidirectional Gated Recurrent Unit (BGRU) network. AE is modified and trained to detect rare failure, and the result from AE is fed into the BGRU to predict the next occurrence of failure. The applicability of the proposed approach is evaluated using real-world test cases of log-based warning and failure messages obtained from the aircraft central maintenance system fleet database and the records of maintenance history. The proposed AE-BGRU model is compared with other similar deep learning methods, the proposed approach is 25% better in precision, 14% in the recall, and 3% in G-mean. The resu...