Your Predictive Maintenance Capabilities will be Enhanced by Big Data Technologies

In most cases, maintenance activities are based on preventive measures, which focus on regular maintenance of machines, components and other infrastructure elements in order to prevent them from failing.

However, these preventive maintenance measures are not always optimal, as they usually perform maintenance earlier than needed, which reduces Overall Equipment Efficiency (OEE).

As a result, infrastructure operators and maintenance engineers are increasingly considering a shift towards predictive maintenance. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Implementation of predictive maintenance practices thus leads to optimized maintenance activities.

Understanding Predictive Maintenance Technologies

What is predictive maintenance technology?

Predictive maintenance technology leverages data-driven techniques to forecast when equipment failures might occur, allowing for timely intervention to prevent unscheduled downtimes. By utilizing sensors, historical data, and advanced analytics such as machine learning, it can predict the future condition of machinery and components, ensuring maintenance tasks are conducted at the most opportune moment to optimize efficiency and minimize costs. 

How does predictive maintenance work?

Predictive maintenance relies on continuous monitoring of machinery and system data, collected through various sensors. This data is analyzed in real-time or over time to detect patterns and trends that indicate impending failures. When anomalies are detected, predictive maintenance systems trigger alerts, recommending targeted maintenance actions. The goal is to address potential issues before they result in equipment breakdown, thus enhancing equipment reliability and lifespan. 

The evolution of predictive maintenance technologies 

Predictive maintenance has evolved from simple condition-based monitoring, using basic sensors, to more sophisticated techniques involving Big data analytics, machine learning, and AI. Initially limited to vibration or temperature monitoring, modern systems now incorporate a wide range of detection modalities and deep learning technologies to make more accurate predictions and provide actionable insights. This evolution is supported by the growing integration of industrial IoT (IIoT) and cloud computing. 

Types of Predictive Maintenance Techniques

How many types of predictive maintenance are there?

There are several types of predictive maintenance techniques, each employing different methods to monitor equipment health and predict failures. Common types include: 

• Vibration analysis: Detects changes in vibration patterns that may indicate wear, imbalance, or misalignment in rotating machinery.
• Oil analysis: Monitors the condition of lubricants to detect contaminants, wear particles, and changes in viscosity, which can signal mechanical issues.
• Thermal imaging: Uses infrared cameras to detect abnormal heat signatures in equipment, which can indicate issues like electrical faults or mechanical friction.
• Ultrasound: Detects high-frequency sound waves that are not audible to humans, often used to identify leaks, electrical discharge, or mechanical issues.
• Motor circuit analysis: Analyzes the electrical signals of motors to detect faults in windings, insulation, and circuits, which can prevent costly motor failures.
  
  

Predictive Maintenance (PdM): The Benefits

Many plant operators consider predictive maintenance the ultimate maintenance vision, providing many benefits, including:

• Higher OEE, as predictive maintenance results in a smaller frequency of repairs, when compared to unscheduled or poorly scheduled maintenance.
• Reduced labor and equipment costs, given that an optimal scheduling of repairs can reduce costs for components and minimize the labor needed for the repair process.
• Improved productivity for employees, who will benefit from insights to replace the right component at the right time.
• Increased safety, as the timely monitoring and fixing of potential problems will enable employees to work under safer conditions.
• Reduced unplanned downtime, as potential issues are identified and addressed before they lead to equipment failure. 
• Enhanced equipment reliability, by preventing unexpected breakdowns and maintaining consistent operational performance. 
• Prolonging asset life, as timely interventions prevent excessive wear and tear, extending the lifespan of machinery and components. 

Overall, predictive maintenance will minimize planned downtimes, while leading to better management of employees’ time and effort.

Barriers to Adoption of PdM

Despite the acknowledged benefits of predictive maintenance, its adoption is still in the early stages because the successful deployment is particularly challenging from both a technical and an operational perspective. Some of the main challenges include:

1. The need to combine multiple maintenance detection modalities and associated datasets.

   Predictive maintenance hinges on processing multiple datasets, notably those associated with different sensors and maintenance detection modalities such as vibration analysis, oil analysis, thermal imaging, acoustics and more.
   
   While a single modality (e.g., vibration) can be used for predictions, the consideration of holistic datasets from multiple modalities leads to more stable and accurate predictions. However, the combination of multiple sensors requires additional investments in sensors and data collection and generally doesn’t happen, even in state-of-the-art maintenance systems.

2. Data fragmentation.
   

   Even when data from multiple sensors are available, they tend to be isolated from each other in disaggregated “data islands.” This means that data resides in different systems (e.g., in CMMS like IBM Maximo or dedicated sensor databases) and are represented in diverse formats, which makes their integration challenging.
   
   Semantics across platforms and systems tend to be different, which is yet another factor that hinders their unified processing toward predicting when a machine or component will fail.
   
   Furthermore, there is no easy way to leverage additional data (e.g., quality of the measurements of a metering system, health data of an SCADA or Supervisory Control And Data Acquisition system), which could possibly enrich sensor data toward more accurate predictions.

3. Lack of proper analytics algorithms and tools.
   

   The predictive analysis of large datasets requires advanced algorithms and tools beyond baseline machine learning and statistical models. Such algorithms are not widely available and their deployment and consequent business analysis require the engagement of skilled data scientists who are high in demand but low in supply.
   
   

A recent report from McKinsey looked at data coming from approx. 30,000 sensors on an oil rig and found that 99% of the data was unused. The 1% (or less) of data used was mostly to detect and control anomalies and not for optimization and prediction, which provide the greatest value. (source: Agência Brasil)

Big Data Technologies to the Rescue

The advent of Big Data technologies provides the means for overcoming the above challenges.

Big Data is generally an overhyped term which is sometimes used as a marketing pitch but predictive maintenance provides an ideal business case for deploying Big Data technologies. Big Data is about developing and deploying distributed, data-centric systems that extend the capabilities of state-of-the-art databases in order to handle datasets featuring the four Vs:

Volume:

Large volumes of data, typically volumes exceeding the capabilities of conventional database systems.

This is highly relevant to predictive maintenance systems, as several oil or energy plants produce maintenance datasets at rates close to several terabytes (TB) per week. A jet engine equipped with some tens of sensors could generate over 1 TB of maintenance-related information every day.

Variety:

Data of extreme variety refers to highly heterogeneous data featuring different formats and semantics. In predictive maintenance, this is the case with the diverse “data islands” outlined above.

Utility companies collect and analyze datasets from multiple systems and data sources such as SCADA (Supervisory Control and Data Acquisition), EAM (Enterprise Asset Management), online monitoring, and weather information systems. The data of these systems are provided in different formats, such as eXtensible Markup Language (XML), Comma Separated Values (CSV), JavaScript Object Notation (JSON), and text document. Each format features different semantics.

Big Data platforms enable the integration and unification of these data streams in order to enable predictive analytics for maintenance services.

Velocity:

Data of high velocity, which refer to streaming datasets that feature very high ingestion rates. In predictive maintenance, such data should be used to rapidly update maintenance information when the condition of a component or machine changes. The handling of high-velocity data enables real-time insights on maintenance tasks.

In the oil and gas industry, for example, it will become common for companies to deploy and use many hundreds of oilfield sensors to derive insights on operations. In the scope of such deployments, Big Data platforms will provide support for the ingestion and analysis of millions of data points per second, in order to get instant insights on the health of assets and avoid unplanned downtime.

Veracity:

This refers to datasets that are characterized by uncertainty. This is clearly the case with sensor data, which can be noisy and in need of statistical processing.

For instance, during the collection of data about the condition of an air compressor, a predictive maintenance system must be able to deal with data veracity through recognizing errors, incompleteness and imprecision. Big Data platforms provide the means for identifying such errors and imprecision.

By handling the four Vs, Big data technologies alleviate the data challenges of predictive maintenance through facilitating the unification, integration and real-time processing of very large, maintenance-related datasets. They pave the way for predictive analytics that can provide credible insights on the condition of equipment and subsequently facilitate the anticipation of failures. Predictive analytics are also propelled by the rise of Deep Learning technologies. These provide the means for processing not only numeric sensor data, but also multimedia data, such as imaging data and information from acoustic sensors. Deep Learning goes hand in hand with Big Data, as it is usually deployed in conjunction with BigData infrastructures and technologies.

Big Data is Vital to Successful PdM Adoption and Implementation

Big data technologies are certainly key elements in realizing the transition from preventive to predictive maintenance based on an optimal exploitation of available datasets.

They can derive insights on the condition of the equipment, including hidden patterns of maintenance. However, they can go much further to realize the full potential of predictive maintenance. Future predictive maintenance systems will be able to close the loop back to the plant floor through actions such as configuring devices or even stopping engines.

Such actionable intelligence will be achieved based on the integration of Big Data technologies with industrial automation.

Overall, the big data revolution will enable plant operators and maintenance experts to complete the transition from preventive to predictive maintenance.

This evolution will not be only a matter of technology deployment, but also a matter of investment in complementary assets, such as new maintenance processes and employee training. The era of big data analytics and predictive maintenance is fast approaching and we should be taking steps to prepare for the transition.

Examples of Predictive Maintenance in Action

What is an example of predictive maintenance?

An example of predictive maintenance is using vibration analysis to monitor the condition of rotating equipment like pumps or motors. For instance, by continuously measuring vibrations, maintenance engineers can detect imbalance, misalignment, or bearing wear long before these issues cause a breakdown. This early detection allows for timely repairs, preventing equipment failure and minimizing unplanned downtime. 

Case studies from various industries

LSB Industries, a manufacturer of ammonia and fertilizer products, faced common challenges like unplanned outages and downtime. Implementing Prometheus Asset Performance Management (APM) allowed LSB to start using predictive maintenance despite having limited data. In just two months, the pilot program proved its value by preventing downtime, and within a year, the program delivered a 5x ROI. This success was achieved by using existing data streams to proactively identify and prevent critical failures, avoiding multiple days of lost production. Download the LSB Industries Case Study.

Meanwhile, Great River Energy’s Coal Creek Station, a power generation facility, struggled with maintaining reliability due to the complexity of its operations. After introducing Prometheus Group’s predictive analytics platform, the plant drastically improved its preventive maintenance program. Predictive maintenance allowed the engineering team to detect and resolve more than 320 issues in three years, significantly reducing downtime and extending the life of plant assets. The cost savings and operational efficiencies achieved through early detection and diagnostics brought substantial long-term value. Download the Great River Energy Case Study.

Real-world applications and outcomes

Predictive maintenance in real-world applications has consistently shown its ability to avert costly downtime and optimize operational efficiency. For instance, LSB Industries reduced outages in their ammonia plants, while Great River Energy extended asset life and decreased maintenance costs by identifying problems before they led to breakdowns. Both cases demonstrate the power of predictive analytics in large, asset-intensive industries. 

Steps to Implement a Predictive Maintenance Program

Assessing current maintenance practices 

Before implementing a predictive maintenance program, organizations must evaluate their existing maintenance strategies. This involves reviewing failure histories, analyzing downtime data, and identifying critical assets that would benefit most from predictive techniques. 

Integrating predictive technologies 

Once the assessment is complete, the next step is to integrate predictive technologies. This typically includes installing sensors on critical equipment, setting up data collection systems, and deploying software to analyze the incoming data. 

Training staff and stakeholders 

For predictive maintenance to succeed, employees and stakeholders need to be trained on the new technologies and processes. This includes educating maintenance personnel on interpreting predictive insights, as well as helping management understand the cost-benefit analysis of predictive maintenance. 

Continuous monitoring and adjustment 

A predictive maintenance program is not a set-it-and-forget-it solution. It requires continuous monitoring and fine-tuning to ensure its success. Maintenance schedules and predictive models should be regularly updated to reflect new data, changing conditions, and evolving technology. 

Leveraging Prometheus Group for Predictive Maintenance Success

Predictive maintenance offers immense potential to enhance operational efficiency, reduce unplanned downtime, and extend asset life across various industries. The key to unlocking these benefits lies in the strategic use of big data technologies, advanced analytics, and seamless integration with existing systems. Prometheus Group provides the tools and expertise needed to overcome the traditional barriers to predictive maintenance adoption, including data fragmentation and the complexity of analytics. 

By harnessing the power of Prometheus Group’s Asset Performance Management (APM), organizations can transition from reactive or preventive maintenance strategies to a more proactive and data-driven approach. This shift will not only optimize maintenance efforts but also result in significant cost savings, improved safety, and increased equipment reliability. To learn more about Prometheus Group’s range of solutions, including APM, sign up for a free, personalized demo today!