Smart Predictive Maintenance for Industrial Equipment Using

Supervised Machine Learning

K. T. Thilagham

, S. Duraithilagar

, P. Mathiyalagan

, M. Silpa Raj

Sathyamoorthy N.

and P. Jaisankar

Department of Metallurgical Engineering, Government College of Engineering, Salem‑636011, Tamil Nadu, India

Department of Mechanical Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayak

Mission's Research Foundation, Salem‑636308, Tamil Nadu, India

Department of Mechanical Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

Department of Computer Science and Engineering (Cyber Security), CVR College of Engineering, Hyderabad‑501510,

Telangana, India

Department of MCA, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Mathematics, Nandha Engineering College (Autonomous), Erode‑638052, Tamil Nadu, India

Keywords: Predictive Maintenance, Supervised Learning, Industrial Analytics, Equipment Failure Prediction, Machine

Learning Framework.

Abstract: This paper proposes a supervised learning based predictive maintenance framework for industrial devices, in

order to reduce unplanned stoppages and advance operating efficiency. Enlisted sensor data and historical

maintenance records are used in training classification and regression model for sensing the signals of

equipment failure in an early stage. The system is designed for a wide variety of industrial environments, and

provides a mechanism for timely decision-making. The entire operation can be rapidly deployed and every

model is transparent. Experiments verify the framework’s practical effect on predicting large failures and its

subsequent improvement of maintenance planning. As a result, unexpected breakdowns are significantly cut

down in number.

1 INTRODUCTION

The reliability and performance of plant machinery

and equipment are crucial for maintaining

productivity, quality, safety. But equipment

accidents, especially when they occur unexpectedly

can have far-reaching results: unplanned downtime,

increasing maintenance costs and loss of revenue.

Workers may also be at risk. These days more

advanced strategies are preferred, however. Times

have changed and so has industry. Many original

implementers still follow the traditional system,

which is to say that if there's a breakdown, periods of

failure are longer than expected for certain. This is

waste.

The widespread adoption of Industry 4.0

technologies and the spread of sensors and networked

devices have made it possible to capture large

amounts of operation and condition monitoring data.

This transformation has laid the groundwork for

predictive maintenance--a way of using historical and

real-time data to anticipate when equipment will fail

so one can schedule its maintenance. Among various

techniques such as supervised learning, which stands

out because it can model complex relationships

between input data (for example vibration,

temperature, pressure) and equipment health status.

This paper proposes a data-driven predictive

analytics framework which adopts supervised

learning algorithms to forecast potential equipment

breakdowns. It employs the framework to handle

historical maintenance logs and sensor data, extract

features that are relevant, and then train various

predictive models such as decision trees, support

vector machines and ensemble classifiers. By

concentrating on ways of identifying and dealing with

the impact of malfunctions, the solution put forward

here serves to better maintenance reliability and

planning.

Thilagham, K. T., Duraithilagar, S., Mathiyalagan, P., Raj, M. S., N., S. and Jaisankar, P.

Smart Predictive Maintenance for Industrial Equipment Using Supervised Machine Learning.

DOI: 10.5220/0013856400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages 5-12

ISBN: 978-989-758-777-1

Moreover, this framework is modular in nature

and can be scaled across various industrial sectors. It

includes components for data pre-processing, model

training, evaluation, and deployment. The approach

underscores model explains ability and transparency

in order to ensure trust and usefulness for

maintenance engineers as well as factory operators.

And in the end, it not only improves critical asset

reliability and life cycle, but also results in less costs,

energy consumption and more environmental,

sustainable industrial practices.

2 PROBLEM STATEMENT

Industrial systems rely on machinery to guarantee

both the operation and safety. But still, the

unexpected failure of even a single component is one

of its greatest challenges, causing thousands or more

in losses to companies and making it hard for them to

produce goods and endangering workers' lives.

Other conventional kinds of maintenance--like

reactive and preventive techniques do not always

work in locating fault accurately or make optimal use

of maintenance calendars. These options can only

react to trouble after it takes place; they are also based

on time limits that do not necessarily reflect the true

situation of the equipment. As a result, there is

unnecessary maintenance and perhaps also associated

risks which simply go unnoticed.

With the increasing complexity of industrial

equipment and the huge volumes of operational data

today’s sensors and IoT devices produce, there is an

emerging demand for intelligent, data- driven

solutions that can accurately predict when and why

equipment might fail.

Although predictive maintenance has been listed

as an effective solution, current models of it generally

lack adaptability, scalability and interpretability.

Another problem lies in the lack of effective

application of supervised machine learning

algorithms that use labeled historical data to provide

accurate and operational advice for different

industrial scenarios.

Problems like noisy data, class-imbalance, model

overfit and the integration of solutions into a real-

world environment limit the practical application of

current methods even more.

We aim to overcome these limitations by

constructing a sound predictive analytics framework

based on supervised learning technology capable of

providing accurate forecasts for equipment

breakdowns. The aim is to improve diagnostic

decision making, reduce unplanned downtime and

provide prescriptive solutions that are in line with

today’s industrial requirements for efficiency,

reliability and operational definitions of excellence.

3 LITERATURE SURVEY

Given the potential to reduce unexpected equipment

failures and increase operational efficiency,

supervised machine learning for predictive

maintenance has captured much attention in the last

decade - it is a natural extension of computer aided

maintenance programs. Baradaran (2025) conducted

a comparative study of supervised learning methods

for electric motor maintenance and found that

decision trees and support vector machines are able to

locate early fault signatures. Following on from this,

Bhavani, Nagarjuna, and Pradeep (2025) investigated

manufacturing machine learning approaches and

found that labelled datasets can improve the accuracy

of fault classification.

Patel and Kalgutkar (2024) used industrial

equipment to show that machine learning models

trained on sensor data can predict anomalies with

high accuracy. The models, however, are confined by

data size. Similarly, Okeke et al. (2023) applied

predictive maintenance techniques in a Nigerian

industrial context, showing how local adaptations

influence model performance. A broader view was

offered by Satwaliya and Satwaliya (2025), who

presented an extensive review characterizing the rise

and current state of predictive maintenance for

different industrial sectors.

Zhao et al. (2023) put forward a transformer-

based reinforcement learning model for prescriptive

maintenance, which has a high level of complexity

but promises to give much greater accuracy. Hamaide

et al. (2022) built a two-step learning system which

tested several supervised learning formulations

simultaneously, pointing the way to multi-algorithm

integration. Gonzalez and Lee (2025) showed the

success of supervised learning in detecting

compressor faults, dwelling on techniques obtainable

Ramesh and Singh (2024) then expanded this analysis

to the oil and gas sector.

As seen from Smith and Kumar (2025), Barnett

(2025), Johnson and Wang (2025) (2024a),

interpretability in artificial intelligence has

exceptional relevance to those responsible for expert

system preventative maintenance Industry facing

reviews by Miller and Chen (2024), Singh and Rao

(2025), and Kumar and Patel (2024) gave some

specific suggestions for installation. Additionally,

Lopez and Zhang (2023), Davis and Nguyen (2024)

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

showed how IoT integration could be useful in the

industry with real-time surveillance capabilities.

Forecast research on predictive maintenance

products carried out by Thompson and Garcia (2023),

Williams and Lee (2025) showed that PDIMS market

penetration would rapidly increase. Meanwhile,

Anderson and Brown (2024) continued in their

dressing down of performance benchmarking for not

meeting standard or creating clean/fast real-time data

flow. Studies showed up that model individualisation,

mixing types of study and challenging exclusivities

were pressing technical topics such as Taylor and

Singh (2023), Roberts and Chen (2025), Mehta and

Das (2023), Natarajan and Bose, (2022b), and Kim

and Suh (2025).

From the previous discussions, prestige, like stage

props in drama, is obviously out if we are to build

whole kinds of real-world seismic organs, umm,

mannerly models. As a matter of fact, supervised

learning models in predictive maintenance were

found to be most satisfactory. Laying a foundation for

the construction of a versatile, scalable, real-world

maintenance intelligent framework.

4 METHODOLOGY

In this study, a comprehensive and adaptable

methodological approach was used to build a

predictive framework for industrial equipment

maintenance by machine learning. A principal aim of

this approach is to overcome such emergent issues as

broad-based data sources that could cause confusion

during the model-building process. And problems

involving the interpretation of a model after it has

been constructed, Real-time deployment difficulties

Maintenance Horizons accommodate the system is

built on a modular foundation Graphical

programming interfaces have been used to put these

functions together.

The technique begins by gathering labelled

historical industrial equipment data. These data

sources typically comprise sensor readings marked

with time stamps, records of machinery operation,

and maintenance logs. Machine-embedded sensors

continuously monitor key operational parameters,

e.g. vibration, temperature, pressure, current and

load. Historical records are labelled in accordance

with actual maintenance outcomes—whether a

failure occurred or the equipment remained

functional. This gives us the true data used for

supervised learning. Figure 1 shows Training vs

Testing Dataset Distribution.

Figure 1: Training Vs Testing Dataset Distribution.

After you test-drive the car, write down all its

features and what kind of control panel it has.This is

a new idea that I spoon up. It hasn't been put into the

public domain yet, so there might not be any direct

references available online.Keep in mind, please

show respect to my article by noting where it came

from. You don't need credit though.Sure, my efforts

are seen as useful for people making their

decision.Let the older ones know this. I have more

than one temporarily "frozen" installation of Gentoo

Linux and for the interim period before those others

are up again, here is where you can find my back-up

plan in development mode.Wait a moment. There was

homeopathic treatment YEARS ago! (And I had lots

of real benefits from it.) Okay, stay with me until the

conversation is over, then ask me for any remarks you

hear this afternoon.

Feature engineering can significantly convert raw

sensor data into meaningful input for machine

learning models. Mean, variance, skewness, kurtosis

and other statistical indicators are derived application

of domain knowledge in sliding-window areas; time-

series features such as trend, seasonality, lag variables

between t and t-1 are quantified. Signal processing

techniques that help to reveal frequency-domain

characteristics are also employed in industrial

machinery health monitoring and condition

monitoring. Fourier transforms wavelet analysis give

various statistical descriptions of these

measurements, which are then used in data analysis

and improvement. Figure 2 shows Supervised

Machine Learning Workflow for Predictive

Maintenance.

Smart Predictive Maintenance for Industrial Equipment Using Supervised Machine Learning

Figure 2: Supervised Machine Learning Workflow for

Predictive Maintenance.

Figure 3: Sensor Feature Correlation Matrix.

It is either to try out multiple supervised learning

algorithms (such as decision trees, logistic regression,

support vector machines (SVM), random forests and

gradient boosting machines (GBM)) or to discard the

engineered characteristics of an infinite feature set

altogether. Figure 3 shows Sensor Feature Correlation

Matrix. To lower the variability of model structures,

all of these models are generated through cross-

validation. And when training models, stratified k-

fold cross-validation serves both to make them robust

and to prevent overfitting. Hyperparameter tuning

follows the grid reasoning method or utilizes

Bayesian optimization to find the best configuration

for each model.

Model evaluation proceeds using a broad set of

metrics. How many of the predictions are correct—a

major issue for accuracy and F1-score. The

suspiciously high ratio of negatives that turned out to

be positives is captured by precision, while on the

other hand recall shows us when unlucky turns of

phrase become unlucky ones. For ROC-AUC scores,

they are employed in judging the ability of a model to

separate things into different categories or groups.

The model that performs best is selected based on its

metric scores; it must also be interpretable and easy

to apply in the industrial workplace.

In deployment, the trick is to incorporate the

model into a live monitoring system and connect its

sensors with those on equipment. Continuously

collecting new data which feed back into the model

as time goes on, the system supplies its health status

predictions and failure probabilities for a machinery

component or system. The results are displayed to

maintenance teams in visual format on a dashboard

providing actionable insights: alerts, failure risk

grades and recommended interventions. The system

should also be ready to learn on its own. It takes

maintenance data back into the model, that trains and

refines periodically.

This methodological framework ensures not only

that predictive maintenance is both accurate and

timely but also that it is scalable, interpretable and

adaptable to changes in the operation environment. It

promotes a transition from reactive to smart

maintenance practices which will improve equipment

reliability, reduce downtime and achieve sustainable

operational efficiency in industrial plants.

5 RESULTS AND DISCUSSION

The application at an industrial equipment

maintenance resulted in promising performance in

various aspects from the proposed supervised

machine learning-based predictive analytics

framework. Training and testing of the framework

were conducted on real industrial field datasets

collected from internet of things (IoT) sensors and

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

condition monitoring systems. We aimed to evaluate

the performance of various supervised learning

approaches for predicting faults in pieces of

equipment and giving warning ahead of time to

perform preventive maintenance.

During the experiment, we trained several models

including but not limited to Decision Tree, Random

Forest, Support Vector Machine (SVM) and Gradient

Boosting Classifier on the labelled datasets in which

both operational data and historical maintenance logs

were involved. All models were performed by cross-

validation to avoid overfitting. The performance of

the models was evaluated using accuracy, precision,

recall, F1-score, and ROC-AUC, giving a

comprehensive overview of model performance.

The Gradient Boosting Classifier outperformed

other models in all models tested and obtained an

average accuracy of 94.6%, F1-score of 0.91, and

ROC-AUC of 0.96. The proposed model was

especially capable of detecting subtle drifts of

equipment behaviours that led to breakdowns, which

made the monitoring procedure particularly suitable

for early fault diagnosis. The Random Forest model

also proved to be more promising with an accuracy of

92.1%, suggesting that ensemble methods can be

good candidates overall for precipitation issues, given

their capability of dealing with the robustness and the

nonlinear relationship of data. The performance of the

SVM model was satisfactory, however, we had

difficulty with high-dimensional data, due to

imbalanced classes.

A key observation was made from precision and

recall values. Although high precision was desirable

since there would be less false positives (and thus,

less unnecessary maintenance work), high recall was

even more desirable, as it meant potential failures

would not be missed. The other four of them were

well balanced by the Gradient Boosting model, so that

we could use the model to be deployed. Additionally,

interpreting the model was made possible with feature

importance and showed that temperature oscillations,

vibration amplitude, and pressure irregularity were

the most important predictors of equipment

degradation. Table 1 shows Performance Comparison

of Supervised Learning Models.

An important observation was the effect of feature

engineering on the performance of the models.

Models trained on raw sensor readings fared

substantially worse than raw data trained models

utilizing statistical and frequency-domain features.

This highlights the need for importance of domain-

specific pre-treatment in predictive maintenance,

where latent failure signals are often buried under

patterns of the sensor readings. Signal process

methods, such as Fast Fourier Transform (FFT), were

widely used to extract signal characteristics for these

patterns, particularly with respect to rotatory

machineries.

Table 1: Performance Comparison of Supervised Learning Models.

Model

Accur

acy

(%)

Precisi

Rec

all

F1-

Scor

C-

Decisio

n Tree

88.2

0.85

0.83

0.84

0.88

Support

Vector

Machine

89.4

0.87

0.82

0.84

0.89

Random

Forest

92.1

0.90

0.89

0.93

Gradient

Boostin

94.6

0.93

0.90

0.91

0.96

It also went as far as real-time integration and

system responsiveness. The implemented framework,

when evaluated in a simulated industrial work

environment, showed real-time inference

performances with very small latencies (under 0.5

second for each prediction, on average). This

reactive nature of alerts allows for disruption to be

detected early on, giving maintenance teams the

opportunity to act before a breakdown occurs. The

visualization dashboard also offered intuitive and

actionable outputs so user friendly, even non-

technical staff could use it. Table 2 shows

Maintenance Events and Model Prediction

Outcomes.

On the potential of scale the architecture

succeeded with different industrial machines. It was

also possible to retain the saliency of pertinent system

features such as compressors, pumps, motors and

conveyor systems from individual server network

models without significant changes to the architecture

of the core network. This illustrates the modularity

and flexibility of the method, which is key for its

application in different manufacturing and processing

scenarios. Figure 4 shows Model Accuracy and F1-

Score Comparison.

Smart Predictive Maintenance for Industrial Equipment Using Supervised Machine Learning

Table 2: Maintenance Events and Model Prediction Outcomes.

Equip

ment

Actual

Status

Model

Predictio

n Result

Time

Failure

(days)

MCH-

101

Failur

Failure

True

Positive

MCH-

102

Norm

Failure

False

Positive

—

MCH-

103

Failur

Normal

False

Negative

MCH-

104

Norm

Normal

True

Negative

—

MCH-

105

Failur

Failure

True

Positive

Figure 4: Model Accuracy and F1-Score Comparison.

While successful in general, some problems

appeared. There was sometimes a bias in modelling

caused by data imbalance, since there were only a few

failure events in the dataset. In order to overcome this,

a combination of generating additional training data

(via SMOTE Synthetic Minority Over-sampling

Technique) as well as weighted loss functions were

used in the analysis, and this helped to improve the

detection of minority classes. Additionally, although

models provided good accuracy in controlled test

conditions, their performance may change as the test

conditions become more extreme or sensor noise is

added, if not re-trained at certain intervals. Figure 5

shows Real-Time Equipment Health Distribution.

Figure 5: Real-Time Equipment Health Distribution.

Table 3: Model Deployment Response Time in Real-Time

Inference.

Model

Average

Inference

Time

(sec)

System

Latency

(ms)

Deployment

Suitability

Decision

Tree

0.22

150

Moderate

SVM

0.35

210

Limited

Random

Forest

0.28

180

High

Gradient

Boosting

0.31

175

High

Finally, the presented supervised learning

approach showed promising potential to improve the

predictive maintenance area. Table 3 shows Model

Deployment Response Time in Real-Time Inference.

Not only did it attain excellent predictive

performance, but it also met important operational

requirements including interpretability, real-time

alerting, and system scalability. Finally, the dialogue

concludes that it is not only the (best choice of)

algorithm that matters, but also how the performance

of the algorithm crucially depends on the quality of

the data, domain specific feature extraction,

feedback-driven model adaption, etc. TACTY is a bit

tough-sounding, adding to its ruggedness in an

intelligent system – built from the ground up to

revolutionize industrial asset management.

6 CONCLUSIONS

The present study has developed and tested a

supervised machine learning model for predictive

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maintenance adapted to the operating requirements

of industrial machinery. The research revealed that

with the extracted historical maintenance data and

sensor-based operational data, highly accurate and

timely failure predictions can be obtained. Based on

an integrated approach with data pre-processing,

sophisticated feature engineering, and model tuning,

it achieved superior predictive power, but remains

flexible in multiple industrial contexts.

Models including Gradient Boosting and Random

Forest were particularly effective, yielding not only

high predictive accuracy but also dependability in

detecting the naturally occurring early stages of

mechanical degradation. The fact that the framework

supports real-time operation at ultra-low latencies,

also emphasizes its practical relevance in fast

changing industrial environments, where narrow

windows of opportunity to act can have economic

consequences in terms of down-time and equipment

damage.

One clear strength of this work is the modular and

scalable design, as it is only necessary to modify the

overall structure for various classes of machines.

Furthermore, the focus on model interpretability

guarantees that predictions are actionable and

understandable for maintenance workers, which in

turn induces trust and eases integration in current

maintenance processes.

Although there are issues regarding data

imbalance and environmental variance, the

presented approach indicates how data-driven

intelligence is capable of transforming maintenance

strategy from reactive/preventive strategy to fully

predictive systems. In conclusion, this work

demonstrates the high potential of supervised

machine learning in industrial maintenance, as well

as serves as a promising reference for further

exploring the possibility of establishing an

autonomous, self-learning system for maintenance.

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