A Systematic Guide for Predicting Remaining Useful Life with Machine Learning
Abstract
:1. Introduction
- To answer the raised question and provide guidance for readers and ML developers interested in RUL model reconstruction, an overall solution of any RUL prediction problem in a kind of flowchart that simplifies the selection of the appropriate ML modeling process is introduced;
- After model selection, the training methodology instructions are discussed in depth for further explanation;
- To ascertain the reliability of the proposed methodology, the proposed flowchart is justified by some of the most important examples of the recent literature that perfectly match the different cases of RUL prediction;
- To discriminate the different classes of ML models used for RUL prediction, a detailed classification of ML models with the help of the proposed flowchart is thoroughly discussed;
- By adopting the proposed flowchart, model reconstruction is made clearer and easier to be drawn;
- A discussion of advantages, disadvantages, and limitations of some important ML tools from each class is also provided;
- To remedy RUL prediction problems, prospective solutions are proposed.
2. RUL Model Selection Steps
2.1. Model Selection Guidelines
2.1.1. Data Availability
2.1.2. Data Complexity
2.1.3. Data Drift
2.1.4. Model Complexity
2.2. Complete Data
2.3. Incomplete Data
3. RUL Model Training
3.1. Data Processing
3.1.1. SP Preprocessing Techniques
3.1.2. ML Preprocessing Techniques
3.2. Training and Validation
3.3. Evaluation
4. Classification of ML Models for RUL Prediction
4.1. Conventional ML
4.1.1. SVM
4.1.2. KNN
4.1.3. MLPs
4.1.4. ELM
4.2. Advanced DL
4.2.1. LSTM
4.2.2. CNN
4.2.3. DBN
4.2.4. Autoencoders
4.2.5. GNNs
4.3. ECT and SI
4.3.1. PSO
4.3.2. GAs
4.4. RL
4.5. GMs
4.6. DA
5. Discussion
5.1. Conventional ML
- In terms of ML and concerning the SVM tool, the best way to use them is by following a joint classification–regression scheme. The classification process is dedicated to HS splitting followed by HI prediction. Even for complete data where labels are available, HS splitting before RUL prediction is of great advantage.
- KNN can be used either for HS splitting or direct HI estimation. However, it is wastefully recommended for HS splitting process, especially when data is generally based on unsupervised learning paradigms.
- MLPs and ELM are generally used for direct RUL predictions. Indeed, strengthening such simple algorithms in terms of representation learning and kee** their simplicity is of great advantage in reducing computational costs, as in automatic neural networks with augmented hidden layer (Auto-NAHL) theories [5].
5.2. Advanced DL
- Due to the nature of sequentially driven data, recursive or adaptive learning is required when building ML models. Therefore, we found that LSTM is more popular than CNN when dealing with this type of data. Indeed, LSTM has the ability to control the remembering and forgetting process of both previous and current used training samples with the help of specific gates. Such a feature is not available in CNNs original theories, but it can be added as in convolutional LSTMs or CNNs with recurrent gated units. The most important is that LSTM variants are preferable in such situations due to their powerful capability in beating vanishing gradient phenomena rather than other recurrent networks. It should be mentioned that LTSM could be the best way to determine HI, since it is time-series curve fitting, and prediction problems.
- We cannot deny the accuracy of the CNN capability in feature representations. In fact, this is the reason behind using joint CNN–LSTM. In such a situation, the map** features of CNNs more effectively contributes to the separation of scattered data that should have similar representations and characteristics. Accordingly, projecting them on SoH evaluation cases, CNNs are more adequate for the HS splitting and classification process.
- DBNs and AEs are recommended for feature extraction, but they do not have the adaptive learning features of LSTMs. This means that when dealing with such algorithms, explainability in terms of real-world applications is then lacking. Therefore, data dynamism must always be considered in such a situation to ensure system prognosability.
- One of the main drawbacks of using deep networks is the computational burden. Besides, learning hyperparameters’ number is huge, making it very difficult to find optimal solutions.
5.3. RL
- Most of the studied cases were driven from simulation models (i.e., data already exist), which do not reflect real-world application conditions. In this case, studies are conducted for the purpose of collecting necessary conclusions and not for the real-time recording process investigation.
- RL needs a simulation environment where data comes in sequences and when agent actions are simultaneously executed. It is very difficult to afford this in such a situation due to possible agent errors when learning in a real environment. Subsequently, these errors could lead to catastrophic damages and loss of life.
5.4. ECT and SI
5.5. GMs
6. Future Improvements and Opportunities
6.1. Data Characteristics
- Most of the previously discussed datasets in Table 4 are generally obtained through accelerated aging experiments (e.g., bearing and Li-ion batteries), or through simulation models (e.g., C-MAPSS). In this context, the obtained results and constructed model do not appropriately fit the real degradation phenomenon. Therefore, more efforts need to be spent on real-data collection from real-world industrial plants.
- Additional efforts also are needed to provide even more complex, industry-like data for an effective validation when dealing with complex industrial plants. For instance, existing datasets (Table 4) generally do not consider the data heterogeneity phenomena where recorded samples are subject to different constraints of multiple recording rates. Therefore, more effort is needed to address this issue and its impact on the prediction process.
- In real-world applications, data coming from different systems are heterogynous and multisource. Therefore, it is mandatory to provide further data-driven experiments in the context of RUL-based similarity modeling and transfer learning.
6.2. Model Complexity
- Since the RUL prediction process describes a dynamic process that changes over time, offline nonadaptive training algorithms are not appropriate solutions. Therefore, models such as CNN, ELM, and SVM need to consider additive features of dynamic learning, such as GRU for instance.
- Only adaptive algorithms, such as adaptive filters (e.g., least square), LSTM, OSELM, and their variants can be used for RUL prediction.
- GNNs should be further examined to provide insights into their application, specifically where there is an obvious scarcity in their applications.
- For RL, simulation-based virtual reality is helpful to provide more realistic learning rather than traditional off-line simulations.
- For better explaining RUL prediction, it is advantageous to use the early and late prediction evaluation metrics required for conditional maintenance tasks rather than ordinary approximation metrics.
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Reference | Studied Types of ML Models |
---|---|
[11] | Data-driven in general |
[12] | Adaptive learning and data-driven in general |
[13] | DL models |
[14] | Statistical and nonstatistical approaches |
[15] | DL techniques |
[16] | Probabilistic and nonprobabilistic methods |
[17] | DL, GAN, and TL |
[18] | Scalable computational techniques |
[19] | DL and stochastic methods |
[20] | DL models |
[11] | Data-driven in general |
Reference | Studied Types of ML Models |
---|---|
[26] | LSTM |
[27] | LSTM |
[32] | CNN |
[28] | CNN and LSTM |
[29] | LSTM |
[33] | CNN |
[34] | OSELM |
[35] | GRU |
[31] | LSTM |
Reference | Used ML Models |
---|---|
[41] | LSTM |
[42] | RNN |
[45] | Nonlinear stochastic model |
[46] | Thresholding algorithms |
[43] | AEs and MLP |
[44] | Recursive filtering |
[39] | TL, MLPs, and HMM |
[40] | GANs and DL |
[37] | TL, LSTM, and GMM |
[38] | TL and DL |
References | ML Classes | Methods | Datasets/Systems |
---|---|---|---|
[67,68,69,70,71] | Conventional ML | SVM | C-MAPSS, Li-ion batteries, PRONOSITA, and IMS |
[72,73,74,75] | KNN | IGBTs, Li-ion batteries, and other bearings datasets | |
[39,76,77,78,79] | MLP | C-MAPSS, Tool wear, and timing belt in a combustion engine | |
[1,2,81,82] | ELM | C-MAPSS and an integrated modular avionic system | |
[27,88,89] | DL | LSTM | C-MAPSS and other bearings datasets |
[90,91,92,93] | CNN | PRONOSTIA and other bearings datasets | |
[95,96,97,98,99] | DBN | C-MAPSS, wind turbine gearbox, and supercapacitors | |
[100,101,102] | AEs | Li-ion batteries, cutting tool, and other bearings datasets | |
[104,105] | GNN | C-MAPSS | |
[119,120,121] | RL | RL | C-MAPSS, pum** system, DC motor, and shaft wear |
[108,109,110,111] | ECT and SI | PSO | Li-ion batteries and journal bearing seizure |
[113,114,117] | GA | C-MAPSS, Li-ion, IMS, and supercapacitors | |
[40,125,126,127] | GMs | GANs | C-MAPSS, PRONOSTIA, and XJTU-SY |
[37,128,129,130,131] | DA | TL | C-MAPSS and PRONOSTIA |
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Berghout, T.; Benbouzid, M. A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Electronics 2022, 11, 1125. https://doi.org/10.3390/electronics11071125
Berghout T, Benbouzid M. A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Electronics. 2022; 11(7):1125. https://doi.org/10.3390/electronics11071125
Chicago/Turabian StyleBerghout, Tarek, and Mohamed Benbouzid. 2022. "A Systematic Guide for Predicting Remaining Useful Life with Machine Learning" Electronics 11, no. 7: 1125. https://doi.org/10.3390/electronics11071125