What is predictive maintenance and how does it work?

Predictive maintenance is a modern method of maintenance management that makes it possible to predict machine failures, but not just that. It is strongly linked to the development of Industry 4.0, and is gaining popularity in industrial and manufacturing companies, but also in transportation or energy companies.

Predictive maintenance differs from traditional strategies, such as reacting to failures after they occur or performing regular maintenance regardless of the actual condition of machines. Predictive maintenance solutions make it possible to predict potential failures and take preventive measures based on real-time data analysis. This makes it possible to increase operational efficiency, reduce maintenance costs and minimize downtime, such as in production.

Predictive maintenance – for which industries?

Predictive maintenance solutions are used in many industries, such as:

• Manufacturing: ongoing monitoring of machine status minimizes the risk of unplanned downtime, optimizes production processes. For one of our clients, we prepared a solution to optimize the processing of food raw material, which significantly reduced the amount of waste.
• Transport: enables prediction and prevention of vehicle failures, resulting in more reliable and safer fleets.
• Energetics: supports monitoring and management of energy infrastructure, increasing efficiency and stability of energy supply. For our client, we prepared a solution that enabled the prediction of compressor failures.
• We have also supported our client, a construction machinery manufacturer, in predicting the failure of demolition equipment. Among other things, our solutions made it possible to control the performance of the cooling system of the machines during breaks from work, or to detect inefficient operation of the hydraulic system.

The above examples are just some of the applications of systems in the area of predictive maintenance. These solutions are widely used in various companies that implement systems and technologies that characterize the “Industry 4.0”.

What benefits does predictive maintenance bring to your business?

Traditional maintenance methods, such as repairs and preventive maintenance, have limitations:

• Repairs: involve intervention after a failure has occurred, which can lead to significant downtime and high repair costs.
• Preventive maintenance: maintenance performed routinely, regardless of the actual condition of a machine, which can lead to unnecessary costs and downtime.

Predictive maintenance enables real-time monitoring of the actual condition of machines, allowing early detection of problems and taking appropriate corrective action before failures occur. This enables companies to plan maintenance optimally, minimizing downtime and costs.

How do predictive maintenance solutions work?

The operation of predictive maintenance solutions is based on three key elements:

Elements of Predictive Maintenance.

• Machine condition monitoring – data on machine operation, such as temperature, vibration, pressure or part wear, is collected using sensors.
• Data analysis – data collected from sensors are processed and analyzed using advanced analytical technologies such as Big Data analytics and dedicated Machine Learning models. Data processing algorithms identify patterns, anomalies and trends that may indicate potential problems or failures.
• Failure Prediction – based on data analysis, it is possible to predict when a machine may need maintenance intervention or part replacement. This makes it possible to plan corrective actions in a preventive manner before a major failure and downtime in production or operations occurs.

It is worth bearing in mind that the proper performance of predictive maintenance solutions is affected by all of the above aspects.

Therefore, it is important for those responsible for preparing a dedicated predictive maintenance solution not only to focus on the stage of processing and analyzing data, but to get to know it properly in advance.

For example, in one of our realizations for a client, on the basis of data from sensors in the machine, we developed a method for finding and marking deviations from standard values of temperature and pressure. The result is a system that marks measurement points that deviate significantly from the correct values. Only the next stage of the work consisted of event classification using an area of ML called Supervised Learning.

Predicitve Maintenace, Internet of Things, Big Data & AI

Predictive maintenance ma swoje korzenie w czasach rewolucji przemysłowej, podczas której zdano sobie sprawę z potrzeby regularnej konserwacji maszyn. Początkowo stosowano metodę prewencyjną z rutynowymi przeglądami, co prowadziło do zbędnych kosztów i niewykorzystanego czasu pracy maszyn. W latach 70. XX wieku zaczęto rozwijać metodologie przewidywania awarii na podstawie danych, co stało się prekursorem współczesnego predictive maintenance.

Predictive maintenance has its origins in the industrial revolution, during which the need for regular machine maintenance was realized. Initially, a preventive method was used with routine maintenance, which led to unnecessary costs and unused machine time. In the 1970s, methodologies for predicting failures based on data began to be developed, which became the forerunner of modern predictive maintenance.

The development of computer technology in the 1980s and 1990s, such as microprocessors, SCADA systems and CMMS, enabled more detailed collection and analysis of machine data. Since the 2000s, the Internet of Things (IoT) has enabled advanced real-time monitoring of machine conditions, automated data collection and precise analysis and failure prediction.

Advances in areas such as IoT, Big Data and Artificial Intelligence have had a key impact on the development of predictive maintenance:

• IoT – enables continuous collection of data from multiple sources to more accurately monitor and analyze machine performance.
• Big Data – analysis of large data sets allows identification of patterns, anomalies, and failure prediction based on historical data.
• Artificial Intelligence (AI) – machine learning techniques enable advanced predictive models that can predict failures with high accuracy.

These innovations, and in particular the development of AI, will be important for the further development of predictive maintenance, enabling even more accurate monitoring, better data analysis and faster responses to potential problems. Companies that successfully implement these technologies can expect significant improvements in operational efficiency and cost reductions.

How to start implementing predictive maintenance in an enterprise?

Implementing predictive maintenance starts with the proper use of data collected by sensors that monitor the condition of machines.

However, just having sensors is only the beginning. It is crucial that the data is properly prepared and analyzed using the right technologies. This requires advanced knowledge and experience. This is where experts are needed who can identify which data is most relevant and how to correlate them effectively to predict failures with sufficient accuracy and in advance.

Maximizing the potential of sensor data is possible by implementing dedicated machine learning (ML) models that are tailored to the customer’s specific conditions and devices. These models can significantly increase the accuracy of predictions and thus optimize maintenance operations. By entrusting complex data analysis and model development to specialists with both Data Analytics and Data Science expertise, the full potential of predictive maintenance can be realized.