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Predictive Maintenance Analytics


At first glance, maintenance may seem like an odd place to use predictive analytics but in recent times it has emerged as the major hotspot for the same. The use of predictive maintenance allows lowering unplanned reactive approaches and direct industries towards preventive maintenance approach. The predictive analytics tool has a wide implementation horizon that makes it the talk of the town and leverages immense benefits in all types of industry.

Some of the industries that benefitted from Predictive maintenance analytics are – Manufacturing, Food & Beverages, Power & Energy, Construction, and Waste Management. As a result, the maintenance process becomes fully automated and connected in all forms of industry type.

Use Cases of Predictive Maintenance Analytics

Reportedly, the predictive maintenance approach leads to a significant increase in ROI, upto a 30% reduction in maintenance cost and a 45% reduction in downtime. Thus, the extended use cases of predictive and prescriptive maintenance tools are as follows.

Use CaseDescription
Reduced Repair and Maintenance TimeWith the use of predictive maintenance, the time required to repair the plant and machinery gets significantly reduced by 60%. It happens with the help of sensors in the equipment that work on the Machine Learning model and indicates the breakdown period. As a result, helps to fix the damage at the earliest stage.
Providing Precise Asset DataThe ability of the AI-based software to predict the mean time between failures helps to provide managers with cost-effective time to schedule plant maintenance. Alongside, the CMMS-based software indicates when maintenance and continuing operational cost becomes higher than replacement cost. Thus, facilitates decision-making and results in the extended life of an asset.
Increased ROI (Return on Investment)With the continuous maintenance software and services, complex machine breakdowns are avoided. This results in increased productivity and lesser cost involvement in the process, which further leads to greater return on overall investment.
Creation of Healthy WorkplaceSince risk management and safety are the crucial concerns of the managers towards the working staff. Therefore, to make it go seamlessly effective the methodologies of predictive maintenance analytics come into the picture. It helps to avoid accidents by signaling the occurrence of the catastrophic events using its Artificial Intelligence.

Metrics for Predictive Maintenance Analytics

Broadly, predictive maintenance metrics are categorized into two parts – the first one which signals the future events and the one which considers the past events. Thus, these metrics are known as leading and lagging indicators respectively. The effective use of both these metrics helps to convert raw data into actionable information. So, it’s time to focus on some prominent and widely used predictive maintenance metrics.

Overall Equipment Maintenance (OEE)The OEE indicates the productivity of equipment and also provides information regarding how proficiently the maintenance process is being carried out. The success of the OEE depends on several factors such as – equipment quality, availability, and performance. Furthermore, it is calculated as the product of these three key factors. Thus, the higher the OEE, the lesser are the chances of a defect.
Mean Time To Repair (MTTR)It focuses on the maintenance of repairable items. As a result, comes in the role with the start of repair process and continues till the operations are restored. The key role player considers repair time, testing period, and return to the normal operating state. It is calculated as the sum of downtime periods divided by the total number of repairs.
Planned Maintenance Percentage (PPC)The powerful metric provides a percentage representation of time spent on planned maintenance activities in contrast to unplanned ones. In an ideal system, 90% of the maintenance should attribute to planning with the objective to avoid unexpected breaking down. Thus, it is calculated by dividing scheduled maintenance time with total maintenance hours (multiple by 100 to get the percentage figure).
Mean Time Between Failure (MTBF)It is the estimated time between one breakdown to the next one in course of normal operations. Thus, it represents the expected healthy life of equipment before it experiences complete failure. It is considered by dividing the sum of operational time by the total number of failures.

Technologies Used for Predictive Maintenance Analytics

The success of Predictive Maintenance Analytics depends on the prominent technologies that are being put to use. Some of the difference-making technologies used throughout the global industries are.

TechnologyApplication in Predictive Analytics
Vibration AnalysisIn this method, software studies the significant changes arising out of the machine’s standard vibration. It is performed by continuously recording the vibrations accurately with the use of a Machine Learning algorithm.
Infrared TechnologyHere, the focus lies on checking the temperature of the equipment and tracking their operational conditions with greater ease. Alongside, it plays a prominent role in identifying the hotspots in all the electronic equipment and faulty terminations in the circuits too.
Oil AnalysisPredictive Maintenance Analytics is gaining huge importance in the fuel and oil industry where viscosity, wearable particles, and the presence of water are tested in the oil. As a result, based on the talked elements, its use case extends to the transportation industry too.
Acoustic MonitoringIt is usually seen to imitate the hearing abilities of experienced workers who might have been diagnosed with malfunctioning owing to loud sounds. It becomes possible by detecting certain inappropriate sounds beyond the background noise in the industries.
Motor Circuit AnalysisBeing used across a variety of industries, it measures the motor’s stator and rotor, detects contamination and basic faults. Moreover, it is potent to test new motor inventory prior to installation and improves the overall system health too.

Examples of Predictive Maintenance Analytics

Such analytical tools have created their mark in various industries by leveraging essential healthy reforms.

  • CERN, the European Organization for Nuclear Research has recently analyzed that the use of big data analytics on the Large Hadron Collider has been a great reformer. It found that its particle accelerators started operating at their full potential and helped to address faults at the initial stage.
  • Rosneft, another renowned name has also invested a significant portion in predictive maintenance to explore unchartered territories of the Arctic region. As a result, it promoted the production rate in maturing Russian Oilfields. Moreover, the company also collaborated with General Electric in 2013 to develop IoT technology to develop its LNP liquefaction units, petrochemical plants, and refineries.
  • GEMU, the leading name in the manufacturing of valves and automation has also brought IoT and AI-based software to use. It intended to monitor the performance of manufacturing processes for detecting, repairing, or replacing the deteriorating components before suffering immense loss.
  • Repsol has witnessed about a 15% reduction in its maintenance, followed by savings of $200 million in operational expenses. It all became possible with the implementation of Machine Learning, Artificial Intelligence in the form of Predictive Maintenance Analytics.
  • The application range of Predictive Maintenance Analytics in BMW is highly diversified and acts as the key sustainability component of the business group. The company uses predictive maintenance tool for positioning and conditioning of each element involved in the production process. As a result, helps to detect anomalies and identify problems at the nurturing stage.

Predictive Maintenance Companies

The accurate prediction paves the way towards organizational goal accomplishment and contributes to ensuring proper maintenance of the company. It is a result of prominent tools and technologies put to use which helps to bridge the maintenance gap. The providers of these solution-driven tools are the big technology companies who have mastered their proficiency and created a better difference in the world. Ideally, predictive maintenance strategies are a combination of condition monitoring along with analytics algorithms. Thus, the companies focus on providing the softwares blended with both major attributes of predictive maintenance which includes condition monitoring and industrial automation hardware, connectivity, analytics, storage, and the platform. Followed by the users’ adoption these technologies allow organizations to witness 25%-30% gains.


A well–known data analytics company Deloitte addresses and provides a solution for all organizational aspects. It provides solutions for financial risk issues, customer, supply chain maintenance, workforce, and everything else that matters. The company holds great proficiency in the field as a result of which it leverages huge support to maintain all the organizational operations with a strong commitment to business analytics. To let predictive data analytics take a progressive turn Deloitte Belgium also collaborated with IBM business analytics. As a result, the company strengthens the greater maintenance possibilities and boosts strategy implementation in all the business models through its profound software.


It is a British Multinational Information Company that tackles climate changes, embraces automation, and enhances organizational performance through its prominent predictive maintenance analytics software. The leading analytics software of Aveva provides world-class operational data management services for its client companies and empowers their people to create smarter, better, and sustainable business strategies. The analytics solution provided by the company in the form of software focuses on creating healthy engineering, operations, and performance. The company’s partner ecosystem comprises around 5000 business partners globally which certainly contributes to its operational efficiency.

Tensor flow

It is an open-source software library developed by the Google Brain team to enhance internal Google use. Thus, the Tensor flow provides a precise symbolic math library that focuses on creating a healthy data flow and supporting differentiable programming for conducting research. Being based on the algorithms of machine learning and artificial intelligence Tensor flow provides its prime focus to the training and inference needs of the company for establishing deep neural networks. These healthy analytics networks support the range of maintenance functionalities in the organization and help to draw large-scale data prediction leading to progressive strategy formation.


The US-based Technology Company Splunk provides data analytics software services to search monitor and analyzes machine-generated data. The software correlates and indexes information to generate requires alerts, visualizations, and reports for supporting the organizational planning process. Splunk makes the use of machine learning algorithms to understand the data patterns and relatable metrics which in turn creates the intelligent maintenance atmosphere. The horizontal Splunk technology is used to streamline business operations with the help of web analytics software. Thus, Splunk is not just a log collection tool but is a suitable platform for ingesting, parsing, and indexing all machine data types and forming maintenance strategies.


The company provides cloud and Artificial Intelligence-based solutions to ensure predictive asset maintenance in the business world. The automated machine learning solution is known for scaling all the predictive maintenance programs across the industries and leverage support to the production process too. It gives real-time insight into the health of assets which impacts the production process. Thus, takes into consideration, the asset failure and repairmen predictions using the sensor’s signal which are driven by data analytics algorithms. Hence, Presenso is an advanced tool that is deployed in every maintenance activity to avoid potential breakdown and abnormalities.

Predictive Maintenance and Data science

Data science plays a crucial role in creating the healthy framework for predictive maintenance analytics which is the core of every business entity. Since the early detection of potential issues helps to mitigate the loss and arising abnormalities. Therefore, organizations across the world are making the best use of data science for maintenance analytics. The critical factors included in predicting the process or equipment failure are model time, equipment year, and warranty details. The combination of predictive maintenance analytics with data science speeds the maintenance process and structures the business operations. As a result, data science supports predictive maintenance by providing the following advantages.

  • It ensures effective cost maintenance by minimizing the repair time and frequency which often leads to disturbance in the production process too.
  • The predictive data analytics software helps to predict the potential downtime of the equipment. As a result, plays a crucial role in, eliminating equipment malfunctioning, failure, and their vulnerability to disasters.
  • The AI-driven predictive maintenance tool when teamed up with data science algorithms helps to conduct root cause analysis. The analysis serves as the basis for increasing the return on assets by reducing the failure rates.
  • A predictive maintenance algorithm also helps to manage labor efficiency and effectiveness in the long run.

Predictive Maintenance using IOT

The strength of Predictive Maintenance Analytics lies in its blending with the IoT framework. As a result, it leads to effective evaluation and monitoring of the asset which in turn allows business entities to create healthy room for proper prediction and planning. According to the PwC report, on average predictive maintenance analytics leads to reduced operational cost by 12%, improved uptime by 9%, and extended life of the asset by 20%. Moreover, it promotes a safe, healthy, and risk–free ecosystem at the workstation. Considering the potential of IoT, Train Italia decided to invest 50M Euros in the element with the aim to reduce maintenance cost by 130M Euros and enhance customer satisfaction by increasing the availability of the train.

Use of Big Data in Predictive Maintenance

Big Data which is the major grist of the Analytics mill is showing its potential in the manufacturing and marketing pitch of the organizations upto the greatest extend. Thus, the development and deployment of a data-centric system for streamlining the predictive maintenance processes revolve around 4V’s of the datasets – Volume, Variety, Velocity, Veracity. The big data structure allows unification and integration of data streams into boosting the health of the maintenance services. Moreover, it paves the way into providing credible insights for anticipating failures. Being propelled by Deep Learning Big Data acts as the means for processing numeric sensor data along with multimedia data which helps to establish a predictive industrial ecosystem. Thus, big data contributes immensely in completing the process transition from preventive to predictive maintenance and prepare industries for all the backlogs.

Predictive Maintenance and Data Analytics

The maintenance activities in the organization are largely driven by preventive measures which are a result of predictive data analytics. Such activities include maintaining the machines, their components, and other infrastructure elements which act as a hindrance to the effective production process. The preventive measures drawn using data analytics are not just optimal but also enhances Equipment Efficiency As a result, organizations find their operational wellbeing shelter in the predictive maintenance umbrella because of the following considerable reasons:

  • Reduced labor and equipment cost
  • Higher Overall Equipment Efficiency (OEE)
  • Improved Employees’ Productivity and Safety.
  • Decreased Downtime.

Predictive Asset Maintenance Analytics

The predictive maintenance revolves around the fact that service shutdown revolves shall be on the right time neither too late, nor too early. Thus, to predict and understand the maintenance cycle asset maintenance analytics provide strong support. The technological advancement in production processes and the Internet of Things (IoT) pushing its boundaries ever now creates healthy space for assets maintenance. The studies show that maintenance analytics is potent to reduce time spent on maintenance planning by 20% to 50% and increase machinery (asset) efficiency by 10% to 20%. Though the concept of predictive maintenance is a decade old it has accelerated its pace into the industrial ecosystem in recent times. Ideally, predictive maintenance algorithms work in four stages which are as follows:

  • Data Generation and Collection
  • Data Modeling and Relation building.
  • Data Introduction in Production Processes.
  • Data Feedback based on Model Updates.

The adoption of predictive maintenance analytics adds the following strength to the organizational setup.

  • Better Utilization of Service Time.
  • Timely Availability of Spare Parts.
  • Increased effectiveness and efficiency of the machinery.
  • Early Warning Signals against Breakdown.

Predictive Maintenance in Manufacturing Industry

Inarguably, predictive maintenance is becoming the backbone of manufacturing industries in recent times. In such industries depreciation is considered as the vital cost and predictive maintenance is one such prominent way to mitigate the cost type. It ensures better sustainability and longevity of the manufacturing units which allows business entities to save substantial costs for multiple manufacturing processes. Moreover, the algorithm of predictive maintenance also aims to elevate the asset management practices through IoT –enabled technology. The concept of predictive maintenance is quite intuitive and takes into consideration the sensor data for monitoring and analyzing the machine’s condition. Furthermore, readings generated by sensors help to predict potential hazards and maintenance issues in the workflow. As a result, the concept provides to limit unplanned downtime, optimize equipment lifetime, and establish room for leveraging improved strategies.

Predictive Maintenance Using R

Predictive analysis is a branch of analysis that makes the use of statistics operations along with R Language to predict future events. Generally, time series analysis, non –linear least squares are the basic tools for collecting and building relational databases. Thus, predictive maintenance analytics makes the best use of the widely – accepted programming language R for the following beneficial processes.

  • Defining Project
  • Data Collection
  • Data Analysis
  • Validating the Statistics.
  • Framing Predictive Models
  • Deploying models into Production Processes.
  • Monitoring the Model Performance.

Therefore, by helping develop Predictive Maintenance Analytics software R allows business entities to focus on customer behavior, gain competitive command in the market, increase revenue, and identify weak areas.

Final Words

AI and ML-based predictive Analytics has become the mainstay in global operations. Thus, unlocking the potential of predictive maintenance in almost all forms of industries from manufacturing to marine brings great revolution. The analysis process includes data collection from spreadsheets, social media, photos, videos, and databases to predict the information in a cohesive manner and let enterprises enjoy its perks.

Topics in Predictive Analytics

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