Expert Views

Published on Jul 19, 2023

How predictive maintenance can save your time and money

Constantin Brincoveanu

Machine Learning Engineer

Picture this: it’s a bright and promising Monday morning. The assembly line of your factory is humming with productivity. Suddenly, it grinds to a halt. A critical piece of machinery has failed, and production is stalled. The consequences are severe: delayed orders, disappointed customers, and an expensive emergency repair bill.


Now, imagine a different scenario.


What if you could predict this failure before it happened, perform maintenance during non-productive hours, and keep the production line running smoothly? What if downtime became a ghost of the past?


Welcome to the present day. It’s called predictive maintenance.


In this article, we’ll guide you through the “why,” “what,” and “how” of predictive maintenance. We’ll also share insights on the technologies that enable it and reveal the enormous benefits it can bring to your business.


The competitive edge: backed by data

A study by PwC found substantial evidence underscoring the significant value predictive maintenance brings even to companies with sophisticated maintenance functions already. An impressive 95% of companies saw tangible results directly linked to their adoption of predictive maintenance, revealing its vast potential for enhancing multiple value drivers.


Predictive maintenance helps companies to enhance multiple value drivers. Improved uptime stood out as the primary objective, where 60% of companies reap their rewards. Other noteworthy benefits included cost reduction, which on average amounted to 9%, a 14% reduction in safety, health, environment, and quality risks, and an extension of aging assets’ lifespan by 20%.


Surprisingly, a considerable number of respondents also identified gains in areas not typically associated with the primary objective of predictive maintenance. These included a 40% improvement in customer satisfaction, a 37% improvement in product design, and 36% energy savings.


Implementing predictive maintenance reduces downtime by identifying potential issues before they become problems and contributes to equipment longevity by proactively addressing potential problems. The associated reduction in substantial damage lowers the need for costly repairs or replacements.


Additionally, predictive maintenance optimizes inventory management. The ability to foresee when a part might fail eliminates the necessity for large stocks of spare parts, liberating cash and storage space. On top of that, ensuring equipment remains in optimal condition enhances facility safety by reducing the likelihood of accidents that could harm employees and disrupt operations.


In an efficiency-centric world, predictive maintenance has transitioned from being a luxury to a necessity. It’s not just about cost-saving; this strategic tool boosts productivity, heightens customer satisfaction, and strengthens market competitiveness.


Making AI work for you

Predictive maintenance techniques provide real-time insights into equipment conditions to enable the prediction of optimal maintenance timing. That leads to significant cost savings over routine or time-based preventive maintenance.


So, what are the technologies behind predictive maintenance? In short, predictive maintenance is all about the following two steps:


  1. Collecting data from devices.
  2. Leveraging that data for predictive purposes.


The first step of collecting data usually happens right at the device that is being analyzed. A key technology here is the internet of things (IoT), which establishes the framework for real-time data collection from diverse equipment sensors.


The data includes not only the condition of machines and equipment but also their environment, with factors like temperature and humidity being considered. This data, diverse and in various formats, demand high-capacity databases for effective processing.


The quality and reliability of insights heavily depend on the size of the database and the sophistication of the analysis algorithm. Larger databases and smarter algorithms yield more reliable outcomes.


It’s crucial that testing procedures must not damage the device’s functionality. As we already partly sketched out in one of our previous articles, methods for collecting data encompass vibration analysis, acoustic measurements, non-destructive testing (NDT), and ultrasonic testing.


For the second step of leveraging the data for predictive purposes, the key technology is artificial intelligence (AI). AI employs advanced machine learning (ML) algorithms for data analysis and forecasting potential equipment breakdowns.


Using AI to predict the future

The main objectives of machine learning tasks within the domain of predictive maintenance are usually either:


  1. Predicting remaining useful life (RUL).
  2. Predicting health indicators (HI).


As the name suggests, predicting RUL aims to forecast how long a device will continue operating until a severe degradation or failure occurs.


Most RUL prognostics are obtained using supervised ML models trained with many labeled data samples where the true RUL is known. That means that you need to have a sufficiently large dataset of past failures to allow the ML models to learn patterns that lead to future failure.


In many cases, few labeled data samples are available. That can be the case when there’s already a system in place to preventively replace devices before failure or simply when there has not yet been much data collected.


In those cases, health indicators (HIs) play a crucial role. Derived from various types of device data, like vibration for machines or temperature and pressure readings for industrial plants, HIs serve as early warning signals for potential problems.


Deep dive: machine learning technologies

However, creating effective HIs demands a deep comprehension of the system, its failure modes, and the correlation between collected data and system health, this requires a blend of domain expertise, data analysis skills, and knowledge of predictive maintenance methodologies.


Several ML techniques can be effectively employed for predictive maintenance tasks:

  • Multi-layered feed-forward neural networks (FFNs): Trained using stochastic gradient descent with back-propagation, these neural networks consist of multiple layers of nodes in a directed graph. Each layer is fully connected to the next one. They’re particularly adept at handling non-linear relationships and interactions between variables.
  • Autoencoders: A type of neural network, an autoencoder is trained with the same dataset serving as both the input and output. The network has fewer parameters than the dimensions in the dataset, forcing it to learn a compressed representation of the data. In this sense, it’s quite like principal component analysis (PCA), which also works to reduce the dimensionality of data.
  • Feature engineering: This process involves creating new features or modifying existing ones to improve model performance. In predictive maintenance, feature engineering could include creating variables that capture historical trends, anomalies, or changes in sensor readings.
  • Hyperparameter optimization, such as using grid search, for example: The performance of ML models can depend heavily on the choice of hyperparameters. Grid search is a technique for optimizing these hyperparameters by systematically exploring a range of potential values and identifying the combination that yields the best model performance.
  • Long short-term memory (LSTM) autoencoders: These are a specific type of recurrent neural network architecture well-suited for time-series data common in predictive maintenance. LSTMs have a memory cell that enables them to effectively learn and remember over long sequences, making them ideal for detecting anomalies or changes over time.


Our solution leverages these innovative technologies to ensure businesses can swiftly transition to operational status while harvesting the rewards of this initiative-taking approach.


Our solution: your competitive advantage

To anyone who wants to establish predictive maintenance effectively and in the long term in their company, we recommend following these work steps:


  1. Capture, digitize, and transmit data.If needed, we enhance real-time data processing and predictive accuracy within your equipment.
  2. Opt for scalable, secure cloud storage for managing and accessing data.
  3. Thoroughly analyze and evaluate the collected data.
  4. Calculate the expected remaining useful life (RUL) of your key equipment (parts) on an ongoing basis.
  5. Plan the optimal time for the necessary maintenance activities.


A significant challenge with predictive maintenance is managing enormous volumes of data. Drawing reliable insights about the condition of machines and equipment, and promptly detecting potential disruptions, demands the collection, storage, processing, and intelligent analysis of substantial datasets.


Our background in embedded system engineering ensures that we are able to access, consolidate and analyze machine data at scale. If needed, we can deploy our advanced machine learning algorithms directly into your equipment, enabling real-time processing and interpretation of data while significantly improving predictive accuracy and response times.


Robust cloud storage solutions ensure that your data is more than just stored: The flexible storage capacity, combined with advanced security measures, provides a scalable, secure, and accessible home for your data. Integrating cloud storage with our predictive maintenance solution provides a seamless data management system, ready to grow with your business needs.


Tailored to your needs, our predictive maintenance solution takes all these factors into account and delivers customized software that fits the unique needs of your company. Our team collaborates closely with your experts to understand the specific challenges you face and designs a solution uniquely suited to meet these challenges.


Using advanced AI algorithms, our software analyzes data from various sources, predicts potential equipment failures, and helps you schedule appropriate maintenance activities. Together, we’ll devise an optimal architecture to tackle your specific predictive maintenance task and implement a personalized solution. This collaboration ensures our software integrates seamlessly into your processes and yields maximum benefits from predictive maintenance.


By choosing our integrated solution, you’ll get a comprehensive, end-to-end predictive maintenance system that offers low-level embedded system engineering, robust cloud storage, and advanced AI-based optimization from a single, reliable partner. Pave the way now to a future of enhanced operational efficiency and substantial cost savings.


Your journey starts here

In today’s competitive environment, predictive maintenance is not just an option.


It’s a strategic necessity.


By adopting our predictive maintenance solution, you’re not just buying a service: you’re investing in a more efficient, more profitable future for your company.


Ready to embark on this journey towards enhanced efficiency and profitability?


Contact our sales team today to discuss how our predictive maintenance solution can revolutionize your operations and give you the competitive edge you need.