# Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing

^{*}

## Abstract

**:**

## 1. Introduction

- The future condition of the components for PdM planning is predicted using optimized deep learning.
- To improve the prediction in PdM planning, support vector machine (SVM) classification, rather than machine learning algorithms that face complexity when prediction values vary, is used to configure a recurrent neural network (RNN) network for each range of prediction.
- To overcome the problem of overfitting and data redundancy, optimal feature selection is performed using the proposed Jaya-based Sea Lion Optimization (J-SLnO) algorithm. The objective considered for optimal feature selection is to minimize the correlation between two selected features.
- PdM planning performance is improved by modifying an RNN, in which the weight is updated by the proposed J-SLnO algorithm.

## 2. Literature Review

## 3. Procedures for Predictive Maintenance Planning

#### 3.1. Developed Architecture

#### 3.2. Data Cleaning

**Outlier detection**[33]. Generally, outliers are considered noisy data in statistics. Several outlier detection models have been introduced for different applications. Some of these methods are more generic than others. Outliers are data patterns that do not represent typical trends in the data. Here, the filloutliers MATLAB function is used to detect outliers and substitute them using a fill method. The outlier detection is carried out for extracting the residual components and decomposing them in early stage.

**Missing**

**data**[34]. The missing data problem occurs when some values are missing in the data. Handling missing data requires additional computational time, and the problems caused by missing data require more analysis. To fill missing data, the fillmissing MATLAB function is used to fill missing entries in an array with a constant value. The function performs the decomposition of residuals and approximations. Harmonic analysis is used to decompose the elements into residual and approximate components. Then, data reconstruction is carried out. The main value is taken as the approximation component and a noise component is taken as the residual component. After computing the standard deviation and the mean of the residual component, a random number is computed. Then, the noise components or the sum of the approximation components are utilized for filling in missing data. Finally, this model has reconstructed data.

_{u}, u = 1, 2, ……, ND, where ND is the total number of target attributes.

#### 3.3. Data Normalization

_{u}, and the minimum and maximum values for each record are denoted as Dt

^{min}and Dt

^{max}, respectively.

## 4. Objective Model and Optimal Feature Selection

#### 4.1. Objective Model

**Optimal feature selection.**The first objective of the proposed PdM model is to select the features that minimize the correlation coefficient in the feature selection process. Here, optimal features are selected from $D{t}_{u}^{nrm}$, and the objective function of optimal feature selection process is expressed as follows.

**Optimized RNN:**Here, the main objective is to minimize the error between the actual and predicted values. In this case, weight optimization of RNN w

_{e}is performed by the proposed J-SLnO algorithm. The objective function for error minimization is expressed by Equation (4), and the error formula is expressed by Equation (5).

#### 4.2. Optimal Feature Selection

#### 4.3. SVM-Based RNN Range Classification

## 5. Hybrid Metaheuristic Algorithms for Optimal Feature Selection and Classification

#### 5.1. Conventional Jaya Algorithm

^{th}variable for the e

^{th}candidate during the it

^{th}iteration is denoted X

_{j, c, it}and the value is modified using Equation (16).

^{th}iteration, which is in the range [0, 1]. The variable j for the worst and best candidates is denoted as ${X}_{j,worst,it}$ and ${X}_{j,best,it}$, respectively. To recognize the values of X

_{it}, this algorithm attempts to minimize the sphere function value. The corresponding equation is expressed in Equation (17), where the X

_{it}is in the range [−100, 100]. The recognized solution for this benchmark function is 0 for the entire values of X

_{it}. The pseudocode for the conventional JA algorithm is given in Algorithm 1.

Algorithm 1.Pseudocode of conventional JA [38]. | |

Initialize the size of population | |

Find the best and worst solutions | |

Based on best and worst solutions, modify the solutions using Equation (17). | |

If
$$\left({X}_{j,best,it}^{\u2019}isbetterthan{X}_{j,best,it}\right)$$
| |

Update the solutions using Equation (17) | |

Accept and replace the existing solution | |

Else | |

Maintain the previous solution | |

End if | |

If (termination criteria is met) | |

Return the optimal solution | |

Else | |

Find best and worst solutions | |

End |

#### 5.2. Conventional Sea Lion Optimization Algorithm (SLnO)

_{1dr}term is the speed of the sound of the sea lion leader’s calls, and the $s{s}_{1}$ and $s{s}_{2}$ terms are the speed of sounds in air and water, which are expressed by Equation (21) and Equation (22), respectively.

Algorithm 2.SLnO Algorithm [39]. | |||

Start | |||

Population initialization | |||

Choose${\mathit{X}}_{\mathit{r}\mathit{a}\mathit{n}\mathit{d}}$ | |||

Compute fitness function for each search agent | |||

The best candidate search agent that has best fitness is the X | |||

while (t < maximum number of iterations) | |||

Compute $S{\overline{S}}_{ldr}$ using Equation (20) | |||

if $(S{\overline{S}}_{ldr}0.25)$ | |||

If $(\left|C\right|<1)$ | |||

Update the location of the current search agent by Equation (18) | |||

Else | |||

Choose a random search agent $({X}_{rand})$ | |||

Update the location of the current search agent by Equation (25) | |||

Else | |||

Update the location of the current search agent by Equation (23) | |||

Compute the fitness function for each search agent | |||

Update X if there exists any better solution | |||

Return X as the best solution | |||

end while |

#### 5.3. Proposed J-SLNO Algorithm

Algorithm 3.J-SLnO Algorithm. | |||

Start | |||

Population initialization | |||

Choose${\mathit{X}}_{\mathit{r}\mathit{a}\mathit{n}\mathit{d}}$ | |||

Compute fitness function for each search agent | |||

The best candidate search agent that has the best fitness is the X | |||

while (t < maximum number of iterations) | |||

Compute $S{\overline{S}}_{ldr}$ using Equation (20) | |||

if $(S{\overline{S}}_{ldr}<0.25)$ | |||

if $(\left|C\right|<1)$ | |||

Update the location of the current search agent by Equation (18) | |||

Else | |||

Choose a random search agent $({X}_{rand})$ | |||

Update the location of the current search agent by Equation (25) | |||

Else | |||

if $(\left|C\right|<1)$ | |||

Update the location of the current search agent by Equation (23) | |||

Else | |||

Update the position by Jaya algorithm using Equation (16) | |||

Compute the fitness function for each search agent | |||

Update X if there exists any better solution | |||

Return X as the best solution | |||

end while |

#### 5.4. Recurrent Neural Network

_{g}

_{−1}and f

_{g}, respectively. The g

^{th}hidden activation function is the hidden state of f

_{g}of the GRU (Equation (29)).

## 6. Results and Discussion

#### 6.1. Experimental Setup

**Dataset 1**: The aircraft engine dataset was gathered from Github [42]. It included several multi-variate time series data, which ranged from different engines including 100 engines. The lengths of the run varied, with a minimum of 128 cycles and a maximum length of 356 cycles.

**Dataset 2:**Li-ion cells (18,650) with a nominal capacity of 2 Ah were cycled in a range of ambient temperatures (4 °C, 24 °C, and 43 °C), charged with a common CC-CV protocol and with different discharging regimes. The dataset included in-cycle measurements of terminal current, voltage and cell temperature, and cycle-to-cycle measurements of discharge capacity and EIS impedance readings.

#### 6.2. Error Measures

**(i) MEP:**MEP is the calculated average of the percentage of errors between a model’s forecasts and the actual value of the quantity being forecast. In the MEP calculation, Fv is the forecast value, Av is the actual value, j is the number of fitted points, and i is the value added for each fitted point.

**(ii) MAE:**The MAE is a metric for comparing two continuous variables.

**(iii) SMAPE:**SMAPE is a percentage error-based accuracy metric.

**(iv) MASE:**The MAE of the prediction values is divided by the MAE of the one-step naive forecast in the sample to estimate the MASE.

**(v) RMSE:**RMSE is a commonly used metric for comparing values predicted by a model or to estimate observed values.

**(vi) L1 Norm:**The sum of the magnitudes of the vectors in a space is the L1 Norm. Here, the term L is a matrix, t = 1,2,…., n, where n is the size of the matrix.

**(vii) L2 Norm**: The L2 Norm is the shortest distance between two points. It is also known as the Euclidean norm.

**(viii) L-Infinity Norm:**The maximal norm can be used to compute the length of a vector. The L-Infinity norm is also called the Max norm.

#### 6.3. Meta-Heuristics-Based RNN for PdM Planning

#### 6.4. Machine Learning Algorithms for PdM Planning

#### 6.5. Analysis on K-Fold Validation for PdM Planning

## 7. Discussion

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

Abbreviations | Descriptions |

PdM | Predictive Maintenance |

MEP | Mechanical, Electrical and Plumbing |

JA | Jaya Algorithm |

SLnO | Sea Lion Optimization |

J-SLnO | Jaya-based SLnO |

SVM | Support Vector Machine |

RNN | Recurrent Neural Network |

FM | Facility Management |

SMAPE | Symmetric Mean Absolute Percentage Error |

FMM | Facility Maintenance Management |

CAFM | Computerized Aided Facility Management systems |

CMMS | Computerized Maintenance Management Systems |

BIM | Building Information Modelling |

IoT | Internet of Things |

RFID | Radio Frequency Identification |

MASE | Mean Absolute Scaled Error |

CoxPHDL | Cox Proportional Hazard Deep Learning |

TBF | Time-Between-Failure |

CoxPHM | Cox Proportional Hazard Model |

LSTM | Long Short-Term Memory |

RMSE | Root Mean Square Error |

MCC | Matthew’s Correlation Coefficient |

ANN | Artificial Neural Network |

LR | Logistic Regression |

RF | Random Forest |

GRU | Gated Recurrent Unit |

MAE | Mean Absolute Error |

QP | Quadratic Programming |

MEP | Mean Error Percentage |

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**Figure 5.**Analysis of the proposed and conventional meta-heuristic-based RNN for predictive maintenance planning using the aircraft engine dataset for error measures: (

**a**) MEP, (

**b**) SMAPE, (

**c**) MASE, (

**d**) MAE, (

**e**) RMSE, (

**f**) L1-Norm, (

**g**) L2 Norm, and (

**h**) L-Infinity Norm.

**Figure 6.**Analysis of the proposed and conventional meta-heuristic-based RNN for predictive maintenance planning using the Li-ion battery dataset for error measures: (

**a**) MEP, (

**b**) SMAPE, (

**c**) MASE, (

**d**) MAE, (

**e**) RMSE, (

**f**) L1 Norm, (

**g**) L2 Norm, and (

**h**) L-Infinity Norm.

**Figure 7.**Analysis of the proposed and conventional machine learning algorithms for predictive maintenance planning using the aircraft engine dataset for error measures: (

**a**) MEP, (

**b**) SMAPE, (

**c**) MASE, (

**d**) MAE, (

**e**) RMSE, (

**f**) L1 Norm, (

**g**) L2 Norm, and (

**h**) L-Infinity Norm.

**Figure 8.**Analysis of the proposed and conventional machine learning algorithms for predictive maintenance planning using the Li-ion battery dataset for error measures: (

**a**) MEP, (

**b**) SMAPE, (

**c**) MASE, (

**d**) MAE, (

**e**) RMSE, (

**f**) L1 Norm, (

**g**) L2 Norm, and (

**h**) L-Infinity Norm.

Author [Citation] | Methodology | Features | Challenges |
---|---|---|---|

Chen et al. [26] | LSTM | High performance. Designed to store long-term and short-term pattern data. | Requires more time to train. |

Cheng et al. [27] | SVM | Requires less time to solve the problem. More effective than ANNs. | Inappropriate for huge datasets. |

Gohel et al. [28] | SVM | Attempts to maximize margin among closest support vectors. High performance. | Does not perform well if the dataset includes noise. |

Susto et al. [6] | SVM | Best accuracy. Powerful but complex relative to analysis. | Selecting the kernel function is difficult. |

Traini et al. [29] | NN | Best performance. High system reliability. | Hardware dependent. |

Uhlmann et al. [19] | Elbow Method | Used to determine cluster count. Reduces distortion and provides precise cluster count. | Performance can be improved. |

Zenisek et al. [30] | RF | High performance. Ensembles uncorrelated regression trees provided at random using bagging and boosting to fit the provided information. | Very complex to implement and consumes more time. |

Markiewicz et al. [31] | RNN | Reduces computational complexity. Power consumption is reduced. | Computation is quite slow. |

**Table 2.**Overall performance analysis of various meta-heuristic-based RNNs for predictive maintenance planning using the aircraft engine dataset.

Error Measures | PSO-RNN [43] | GWO-RNN [44] | JA-RNN [38] | SLnO-RNN [39] | J-SLnO-RNN |
---|---|---|---|---|---|

MEP | 123.47 | 96.037 | 215.55 | 105.05 | 82.84 |

SMAPE | 1.0408 | 0.75052 | 0.65396 | 0.65884 | 0.51309 |

MASE | 1.3655 | 5.9667 | 4.9249 | 6.0355 | 1.7978 |

MAE | 50.058 | 40.795 | 43.281 | 36.004 | 28.296 |

RMSE | 64.307 | 53.641 | 53.204 | 46.857 | 41.742 |

L1 Norm | 1.64 × 10^{5} | 1.34 × 10^{5} | 1.42 × 10^{5} | 1.18 × 10^{5} | 92,643 |

L2 Norm | 3679.6 | 3069.2 | 3044.3 | 2681.1 | 2388.4 |

L-Infinity Norm | 184.72 | 131.9 | 115.64 | 118.7 | 121.45 |

**Table 3.**Overall performance analysis of various meta-heuristic-based RNNs for predictive maintenance planning using the Li-ion battery dataset.

Error Measures | PSO-RNN [43] | GWO-RNN [44] | JA-RNN [38] | SLnO-RNN [39] | J-SLnO-RNN |
---|---|---|---|---|---|

MEP | 5.4193 | 6.3721 | 3.1873 | 10.32 | 2.7151 |

SMAPE | 0.040926 | 0.049924 | 0.030704 | 0.090627 | 0.025625 |

MASE | 0.7249 | 0.68541 | 0.32876 | 0.80602 | 0.28758 |

MAE | 30.152 | 37.854 | 26.658 | 74.992 | 18.781 |

RMSE | 83.115 | 89.443 | 48.334 | 114.59 | 35.396 |

L1 Norm | 9.35 × 10^{2} | 1.17 × 10^{3} | 8.26 × 10^{2} | 2.32 × 10^{3} | 582.22 |

L2 Norm | 462.76 | 498 | 269.11 | 638 | 197.08 |

L-Infinity Norm | 434.65 | 455.95 | 214.22 | 443.43 | 136.02 |

**Table 4.**Overall analysis of different machine learning algorithms for predictive maintenance planning using the aircraft engine dataset.

Error Measures | NN [45] | KNN [47] | RNN [41] | SVM-RNN [35,41] | J-SLnO-RNN |
---|---|---|---|---|---|

MEP | 128.31 | 143.48 | 173.64 | 106.95 | 82.84 |

SMAPE | 0.58454 | 0.72322 | 0.96011 | 0.64453 | 0.51309 |

MASE | 2.3749 | 0.91535 | 0.96928 | 3.8597 | 1.7978 |

MAE | 33.419 | 42.884 | 56.886 | 35.794 | 28.296 |

RMSE | 38.837 | 54.74 | 74.34 | 49.105 | 41.742 |

L1 Norm | 1.09 × 10^{5} | 1.40 × 10^{5} | 1.86 × 10^{5} | 1.17 × 10^{5} | 92,643 |

L2 Norm | 2222.2 | 3132.2 | 4252.3 | 2809.7 | 2388.4 |

L-Infinity Norm | 99.519 | 137 | 284.49 | 133.34 | 121.45 |

**Table 5.**Overall analysis of different machine learning algorithms for predictive maintenance planning using the Li-ion battery dataset.

Error Measures | NN [45] | KNN [47] | RNN [41] | SVM-RNN [35,41] | J-SLnO-RNN |
---|---|---|---|---|---|

MEP | 25.723 | 16.429 | 54.028 | 5.4725 | 2.7151 |

SMAPE | 0.20028 | 0.12222 | 0.36893 | 0.037398 | 0.025625 |

MASE | 0.57038 | 0.23407 | 0.96412 | 1.153 | 0.28758 |

MAE | 546.08 | 316.3 | 931.43 | 27.119 | 18.781 |

RMSE | 762.47 | 724.61 | 1164.4 | 92.844 | 35.396 |

L1 Norm | 87,373 | 50,608 | 1.47 × 10^{5} | 840.7 | 582.22 |

L2 Norm | 9.64 × 10^{3} | 9.17 × 10^{3} | 1.46 × 10^{4} | 5.17 × 10^{2} | 197.08 |

L-Infinity Norm | 2044.2 | 2284 | 3759.9 | 509.26 | 136.02 |

**Table 6.**K-fold analysis of different optimization algorithms for predictive maintenance planning on both datasets.

Error Measures | PSO-RNN [43] | GWO-RNN [44] | JA-RNN [38] | SLnO-RNN [39] | J-SLnO-RNN |
---|---|---|---|---|---|

Aircraft Dataset | |||||

MEP | 1.8765 | 1.8345 | 1.7773 | 1.7868 | 1.6914 |

SMAPE | 0.021446 | 0.020966 | 0.020312 | 0.020421 | 0.01933 |

MASE | 0.026873 | 0.025767 | 0.025107 | 0.025299 | 0.024076 |

MAE | 1.2525 | 1.2044 | 1.1728 | 1.1848 | 1.1259 |

RMSE | 5.3746 | 5.249 | 5.1909 | 5.1776 | 5.0895 |

L1 Norm | 16,403 | 15,773 | 15,359 | 15,517 | 14,745 |

L2 Norm | 615.06 | 600.68 | 594.04 | 592.51 | 582.44 |

L-Infinity Norm | 22.58 | 24.69 | 32.71 | 19.45 | 15.62 |

Li-ion battery Dataset | |||||

MEP | 1.9501 | 1.8721 | 1.8331 | 1.7551 | 1.6381 |

SMAPE | 0.022287 | 0.021395 | 0.020949 | 0.020058 | 0.018721 |

MASE | 0.049074 | 0.049092 | 0.045265 | 0.044557 | 0.04046 |

MAE | 56.975 | 57.66 | 53.047 | 51.514 | 47.221 |

RMSE | 218.81 | 222.43 | 212.05 | 211.73 | 196.02 |

L1 Norm | 36,521 | 36,960 | 34,003 | 33,021 | 30,269 |

L2 Norm | 5539.9 | 5631.6 | 5368.8 | 5360.6 | 4962.8 |

L-Infinity Norm | 966 | 967.25 | 966.25 | 967.25 | 965.25 |

**Table 7.**K-fold analysis of different machine learning algorithms for predictive maintenance planning for both datasets.

Error Measures | NN [45] | KNN [47] | RNN [41] | SVM-RNN [35,41] | J-SLnO-RNN |
---|---|---|---|---|---|

Aircraft Dataset | |||||

MEP | 1.9739 | 2.0865 | 1.9911 | 2.0197 | 1.6914 |

SMAPE | 0.022559 | 0.023846 | 0.022755 | 0.023082 | 0.01933 |

MASE | 0.027648 | 0.030027 | 0.029407 | 0.028053 | 0.024076 |

MAE | 1.2914 | 1.4027 | 1.3717 | 1.3104 | 1.1259 |

RMSE | 5.3765 | 5.6753 | 5.6403 | 5.4632 | 5.0895 |

L1 Norm | 16,913 | 18,369 | 17,964 | 17,161 | 14,745 |

L2 Norm | 615.27 | 649.46 | 645.46 | 625.2 | 582.44 |

L-Infinity Norm | 26.28 | 27.31 | 36.25 | 22.54 | 18.21 |

Li-ion battery Dataset | |||||

MEP | 1.9111 | 2.0281 | 1.9501 | 2.0281 | 1.6381 |

SMAPE | 0.021841 | 0.023178 | 0.022287 | 0.023178 | 0.018721 |

MASE | 0.048692 | 0.052466 | 0.047627 | 0.049824 | 0.04046 |

MAE | 57.209 | 60.038 | 55.99 | 58.299 | 47.221 |

RMSE | 219.16 | 224.91 | 214.64 | 222.39 | 196.02 |

L1 Norm | 36671 | 38484 | 35890 | 37370 | 30269 |

L2 Norm | 5548.6 | 5694.2 | 5434.2 | 5630.5 | 4962.8 |

L-Infinity Norm | 975 | 970.5 | 973.75 | 973.75 | 965.25 |

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## Share and Cite

**MDPI and ACS Style**

Abidi, M.H.; Mohammed, M.K.; Alkhalefah, H.
Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing. *Sustainability* **2022**, *14*, 3387.
https://doi.org/10.3390/su14063387

**AMA Style**

Abidi MH, Mohammed MK, Alkhalefah H.
Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing. *Sustainability*. 2022; 14(6):3387.
https://doi.org/10.3390/su14063387

**Chicago/Turabian Style**

Abidi, Mustufa Haider, Muneer Khan Mohammed, and Hisham Alkhalefah.
2022. "Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing" *Sustainability* 14, no. 6: 3387.
https://doi.org/10.3390/su14063387