Random Forest-Based Machine Failure Prediction: A Performance Comparison Article Swipe
Predictive maintenance is widely used in modern industrial systems. It helps improve the working life of machines. It also reduces risk and lowers overall operating costs. Many current approaches still face problems when handling both fast processing and balanced performance across different measurements. In this research, twelve machine learning models are tested. These include standard algorithms and deep learning-based solutions. Two manufacturing datasets are used. One has more samples, while the other shows uneven class labels. Important features are selected by applying strict screening. Model parameters are fine-tuned to obtain stable results. To measure how each model performs, several metrics are used—accuracy, precision, recall, F1-score, and ROC AUC. Among all tested models, random forest shows the best results. It reaches a classification accuracy of 99.5%. At the same time, it keeps a good balance between recall and precision. This model works well when data from sensors is imbalanced. It is also strong in handling patterns that do not follow a clear rule. The system is potentially suitable for real-time deployment in industrial machines with rotating parts, as demonstrated on two representative manufacturing datasets. However, broader validation across diverse equipment types is recommended before large-scale adoption.
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Metadata
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- article
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- https://doi.org/10.3390/app15168841
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All OpenAlex metadata
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https://openalex.org/W4413181196Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/app15168841Digital Object Identifier
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Random Forest-Based Machine Failure Prediction: A Performance ComparisonWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-11Full publication date if available
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Y. Yang, Hongjun WangList of authors in order
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https://doi.org/10.3390/app15168841Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/app15168841Direct OA link when available
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Random forest, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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75Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 48 |
| abstract_inverted_index.machines | 172 |
| abstract_inverted_index.patterns | 154 |
| abstract_inverted_index.problems | 31 |
| abstract_inverted_index.results. | 91, 117 |
| abstract_inverted_index.rotating | 174 |
| abstract_inverted_index.samples, | 68 |
| abstract_inverted_index.selected | 79 |
| abstract_inverted_index.standard | 54 |
| abstract_inverted_index.suitable | 166 |
| abstract_inverted_index.systems. | 8 |
| abstract_inverted_index.F1-score, | 104 |
| abstract_inverted_index.Important | 76 |
| abstract_inverted_index.adoption. | 194 |
| abstract_inverted_index.datasets. | 182 |
| abstract_inverted_index.different | 41 |
| abstract_inverted_index.equipment | 188 |
| abstract_inverted_index.machines. | 16 |
| abstract_inverted_index.operating | 24 |
| abstract_inverted_index.performs, | 97 |
| abstract_inverted_index.real-time | 168 |
| abstract_inverted_index.research, | 45 |
| abstract_inverted_index.Predictive | 0 |
| abstract_inverted_index.algorithms | 55 |
| abstract_inverted_index.approaches | 28 |
| abstract_inverted_index.deployment | 169 |
| abstract_inverted_index.fine-tuned | 87 |
| abstract_inverted_index.industrial | 7, 171 |
| abstract_inverted_index.parameters | 85 |
| abstract_inverted_index.precision, | 102 |
| abstract_inverted_index.precision. | 137 |
| abstract_inverted_index.processing | 36 |
| abstract_inverted_index.screening. | 83 |
| abstract_inverted_index.solutions. | 59 |
| abstract_inverted_index.validation | 185 |
| abstract_inverted_index.imbalanced. | 147 |
| abstract_inverted_index.large-scale | 193 |
| abstract_inverted_index.maintenance | 1 |
| abstract_inverted_index.performance | 39 |
| abstract_inverted_index.potentially | 165 |
| abstract_inverted_index.recommended | 191 |
| abstract_inverted_index.demonstrated | 177 |
| abstract_inverted_index.manufacturing | 61, 181 |
| abstract_inverted_index.measurements. | 42 |
| abstract_inverted_index.classification | 121 |
| abstract_inverted_index.learning-based | 58 |
| abstract_inverted_index.representative | 180 |
| abstract_inverted_index.used—accuracy, | 101 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5100357108 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I13591777 |
| citation_normalized_percentile.value | 0.99087983 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |