Predictive maintenance in Industry 4.0: a survey of planning models and algorithms

The Rise of Predictive Maintenance in the Era of Industry 4.0

Digitization of manufacturing has revolutionized the way products are produced in the modern industrial landscape. The emergence of Industry 4.0 (I4.0) has ushered in a new era where the convergence of real and digital systems plays a pivotal role in enabling autonomous, data-driven industrial frameworks. At the heart of this transformation lies the concept of Prognostics and Health Management (PHM), which has gained significant attention from researchers and practitioners in the context of industrial big data and smart manufacturing.

Predictive Maintenance (PdM) has emerged as a promising technique within the PHM paradigm, offering the potential to achieve zero accidents, failures, or shutdowns throughout the production system. By leveraging machine learning (ML) algorithms, PdM enables the automatic identification and investigation of defects, based on the type of data collected. However, the selection of appropriate ML methodologies, data types, and data sizes to effectively implement ML in industrial systems remains a significant challenge. Inadequate PdM approaches, datasets, or data sizes can lead to time loss and impractical maintenance scheduling.

To address these challenges, academics and practitioners should conduct a comprehensive literature survey on existing studies and ML applications in PdM. This will help identify suitable ML methodologies, data quantities, and data types to develop effective ML solutions for predictive maintenance. The vast amount of data generated by various IoT devices in the industry can be leveraged to gain meaningful insights on equipment Remaining Useful Life (RUL), anomalies, strategies to avoid unplanned downtime, optimize maintenance resources, and more.

Predictive Maintenance Planning Model: A Structured Approach

The PdM planning model encompasses five key stages: data cleansing, data normalization, optimal feature extraction, decision modeling, and prediction modeling. (Figure 1)

1. Data Cleansing: This initial step involves identifying and addressing issues in the data, such as missing values and outliers, through techniques like abnormality recognition and value replacement.

2. Data Normalization: The cleaned data is then normalized, transforming the features into a standardized range to prevent larger values from dominating the smaller ones. This ensures equal numerical contribution from all data characteristics.

3. Optimal Feature Extraction: Feature selection (FS) is a crucial pre-processing step that identifies and retains the most relevant features while removing redundant and insignificant ones. FS techniques can be categorized into three main groups: filter, wrapper, and hybrid approaches.

4. Filter Techniques: These methods assess the significance of features based on their inherent properties, independently of the classifier algorithm.

5. Wrapper Techniques: These techniques build models from scratch for each feature subset, using the classifier’s predictive performance as the evaluation criterion.

6. Hybrid Techniques: These approaches combine the benefits of both filter and wrapper methods, selecting features that emerge during the training process based on the classifier’s evaluation standards.

7. Decision Modeling: The decision-making process in PdM is driven by sensors-enabled, dynamic predictions that provide early recommendations for planned repairs and methods to minimize the impact of expected malfunctions. This is facilitated by the P-F interval, the time between the occurrence of a likely breakdown and its transformation into an operational failure, which allows decision-making algorithms to suggest actions to prevent or mitigate the predicted malfunction.

8. Prediction Modeling: The final stage involves the development of predictive models using various ML algorithms, including supervised, unsupervised, and semi-supervised techniques. These models can be categorized into classification, regression, clustering, and dimensionality reduction approaches, each with its own strengths and applications.

Supervised Learning Techniques for Predictive Maintenance

Supervised learning algorithms are widely employed in PdM applications, as they can handle multidimensional, large feature information and uncover latent relationships across datasets in complex scenarios. Some of the key supervised learning techniques used in PdM include:

Regression Models:
– Linear Regression: Identifies and predicts the relationship between a dependent variable (output) and one or more independent variables (features).
– Lasso and Ridge Regression: Serve to create models with large datasets by minimizing the model’s complexity and preventing overfitting.
– Elastic-Net Regression: Combines the principles of Lasso and Ridge regression, providing a dynamic technique for feature selection and regularization.

Classification Models:
– Logistic Regression: Determines the statistical significance of independent variables in relation to the probability of a binary outcome.
– K-Nearest Neighbors (KNN): A versatile algorithm that can be used for both regression and classification tasks, based on the concept of nearest neighbor classification.
– Naive Bayes: A probabilistic classification technique that relies on the Bayes theorem to determine the likelihood of an occurrence based on past knowledge of potential confounding variables.
– Linear Discriminant Analysis (LDA): A well-known dimensionality reduction technique that projects high-dimensional data into a low-dimensional space while maintaining strong class separability.

Unsupervised Learning Techniques for Predictive Maintenance

Unsupervised learning approaches are also valuable in the PdM domain, as they can extract insights and uncover hidden patterns from unlabeled data. Some of the key unsupervised learning techniques used in PdM include:

Clustering Techniques:
– Density-Based Spatial Clustering (DBSCAN): Groups spatial datasets with different densities and shapes, identifying noise, border points, and core points.
– Hierarchical Agglomerative Clustering (HAC): A bottom-up clustering technique that starts with an initial segmentation and iteratively merges the most similar clusters.
– K-Means: A widely used unsupervised clustering algorithm that partitions the dataset into groups based on similarities, minimizing the total squared distance between each data point and its assigned centroid.
– Fuzzy C-Means (FCM): A robust unsupervised technique that allows data points to belong to multiple clusters with varying membership degrees.

Dimensionality Reduction Techniques:
– Principal Component Analysis (PCA): A widely adopted unsupervised method that reduces the dimensionality of data by identifying the most significant features that capture the majority of the variance in the dataset.
– T-Distributed Stochastic Neighbor Embedding (t-SNE): A nonlinear dimensionality reduction technique that effectively projects high-dimensional data onto a 2D or 3D plane, preserving the local structure of the data.
– Autoencoders (AEs): Neural networks trained in an unsupervised manner to learn compact representations of the input data, which can be used for dimensionality reduction tasks.

Semi-Supervised Learning Approaches in Predictive Maintenance

Semi-supervised learning techniques aim to integrate the objectives of both supervised and unsupervised learning, leveraging a combination of labeled and unlabeled data to enhance the efficiency of the learning process. Some of the semi-supervised learning approaches used in PdM include:

• Self-Training: A technique where a supervised classifier is trained on labeled data and then used to generate pseudo-labels for unlabeled data, which are then added to the training set for further iterations.
• Semi-Supervised Support Vector Machines (S3VMs): An extension of the traditional SVM algorithm that can utilize both labeled and unlabeled data to construct the optimal classification hyperplane.
• Generative Adversarial Networks (GANs): A framework that trains two neural networks, a generator, and a discriminator, in an adversarial manner to produce synthetic data that is indistinguishable from the real data.

Explainable AI for Predictive Maintenance

The incorporation of Explainable AI (XAI) techniques into PdM approaches represents a significant advancement in the domains of asset management and industrial maintenance. XAI methods, such as SHAP, LIME, and feature importance, augment the visibility and comprehensibility of ML models, enabling practical understanding of the underlying causes contributing to equipment deterioration or failure.

By increasing the interpretability and transparency of data-driven models, XAI facilitates the implementation of data-driven PdM systems in smart factories. XAI-based diagnosis tools can analyze anomalous signals identified in the training data, allowing experts to recognize and diagnose abnormalities in a semi-supervised fashion.

Conclusion and Future Directions

PdM remains a crucial strategy for boosting productivity in any industrial setting with devices that degrade over time. With the proliferation of IoT, the potential for producing and installing inexpensive, connected sensors will continue to grow, and ML techniques can be effectively leveraged to implement PdM as the volume of data and the number of sensors increase.

Future research in the area of PdM under I4.0 should focus on the following directions:

1. Exploring the integration of ML and deep learning approaches to fully realize the potential of complex and nonlinear interactions in industrial data.
2. Developing innovative Fault Detection and Diagnosis (FDD) systems that combine data-driven techniques with domain expertise to improve the understanding and reliability of problem-detecting methods.
3. Evaluating and assessing PdM models through the establishment of standardized criteria and benchmarks to create a more uniform and comparative environment in the industry.
4. Investigating continuous degradation monitoring and operational impact modeling to create adaptive PdM models that can evolve over time to reflect changes in operating conditions and system behavior.
5. Exploring the potential advantages of edge-cloud integration for maintenance prediction, leveraging edge computing devices for early data preparation and real-time decision-making.

By embracing these future directions, industry professionals can further optimize maintenance strategies, enhance operational efficiency, and stay competitive in the era of Industry 4.0.