What is Predictive Maintenance?

Predictive maintenance is an approach used by enterprises to predict future failure points as well as monitor the condition of an asset in real time.

Top 10 Predictive Maintenance Startups are:

Actility
Altizon Systems
Arundo Analytics
Asystom
Atos SE
Augury
Bosch Rexroth
C3IoT
Clover Group
Delfos

Besides passive monitoring, the predictive maintenance technique leverages the benefits of Machine Learning (ML) algorithms that take critical historical data, such as temperature, pressure, and vibration, as an input for providing prediction related to the condition of an asset in real time. Prediction enables enterprises to significantly reduce unplanned machine downtime and decide whether any particular asset needs maintenance. Predictive maintenance also ensures machines are taken for maintenance to reduce production losses.

Competitive Leadership Mapping

VISIONARY LEADERS

Visionary leaders in the predictive maintenance startups market are the leading market players in terms of new developments such as product launches, innovative technologies, and the adoption of growth strategies. These players have a broad product offering that caters to most of the regions globally. Visionary leaders primarily focus on acquiring the leading market position through their strong financial capabilities and their well-established brand equity.

DYNAMIC DIFFERENTIATORS

Dynamic Differentiators in the predictive maintenance startups market are established players with very strong business strategies. However, they have a weaker product portfolio compared to the visionary leaders. They generally focus only on a specific type of technology related to the product.

INNOVATORS

Innovators in the predictive maintenance startups market are vendors that have demonstrated substantial product innovations as compared to their competitors. The companies have focused on product portfolios. However, they do not have very strong growth strategies for their overall business, when compared with the visionary leaders.

EMERGING COMPANIES

Emerging companies in the predictive maintenance startups market have niche product and service offerings. Their business strategies are not as strong as that of the established vendors. The emerging vendors include the new entrants in the market, emerging in terms of product portfolio and geographic reach, and require time to gain significant traction in the market.

What drives the Predictive Maintenance Startups market?

Increasing use of emerging technologies to gain valuable insights

IoT devices generate a huge amount of data from various sources, such as sensors, cameras, and other connected devices. The data, however, does not provide any value by itself unless anybody converts it into actionable, contextual information.
Big data and data visualization techniques enable users to gain new insights through batch processing and offline analysis.
The main role of the predictive maintenance technology is to investigate huge volumes of data produced by various components of the IoT ecosystem and transform the data into meaningful insights.
Enterprises are integrating predictive maintenance into their predefined analytical models to automate the data interpretation process and gain real-time insights from the data generated from these IoT devices

Growing need to reduce maintenance cost and downtime

Companies are leveraging predictive maintenance technology to achieve incredible precision, accuracy, and speed over traditional business intelligence tools to analyze IoT data. With the advent of predictive maintenance, enterprises can make operational predictions up to 20 times faster and with greater accuracy than threshold-based monitoring systems.
With AI algorithms applied to the gathered data, business owners can detect potential issues and fix these issues in advance. The system is gradually taught to recognize external and internal factors that have an impact on machine operations.
In various verticals, such as industrial manufacturing or offshore oil and gas, unplanned downtime arising from equipment breakdown can cost money.
Predictive maintenance startups offer enterprises with predictive maintenance applications to predict equipment failure ahead of time.
As industrial customers become increasingly aware of the growing maintenance costs and downtime caused by unexpected machinery failures, predictive maintenance startups are gaining even more traction.

Importance of Predictive Maintenance solutions

Reduces Maintenance Costs - Predictive maintenance startups help businesses in reducing maintenance costs, improves equipment life, decrease downtime and enhances production quality.
Forecasting - Predictive maintenance helps businesses to forecast future outcomes using historical data. However, it is important to understand that data preparation and data quality are the prime inputs for any predictive model. Better the quality higher is the accuracy.
Offers Total Visibility- The tools used in predictive maintenance to assess an asset’s performance are capable enough to identify equipment issues that aren’t easily noticed by expert observation. This compares asset indications to a precise maintenance activity, reducing maintenance costs and offering better visibility.
Very Reliable– Predictive Maintenance decides when maintenance is required based on the asset’s condition. This decreases the downtime and improves productivity by assuring that a piece of equipment remains operating until right before a forthcoming failure.
Enhances Customer satisfaction - Predictive Maintenance systems send out automated notifications that remind customers when it’s time to replace parts and also suggests maintenance services at specific intervals which helps businesses in differentiating their product from others in market.

Important Predictive Maintenance use cases

Use Case 1

Project Objective - To reduce unplanned downtime and failure occurrence of compressors.

Challenge - The company was facing increased downtime and cases of compressor failures. It needed to enhance its compressor performance, and this could be achieved by continuously monitoring the compressors, leveraging predictive analytics, and running diagnostics to identify which components needed to be changed or serviced.

Solution

The company deployed IoTConnect (Softweb Solutions’ IoT platform), which sends operational data to the platform where it is analyzed thoroughly to understand better how the equipment is functioning.
The platform uses remote monitoring, smart rule configuration, ML algorithms, anomaly detection, and data trend identification to continuously report the operating status of the compressors’ internal parameters (oil temperature/oil pressure/stage 1 temperature/motor current/system pressure) over a secure internet connection.
If any operating parameter indicates abnormal behavior, the maintenance personnel are alerted through email notifications for quick decision-making.

Benefits

Avoid unexpected downtime with easy access to equipment information, such as maintenance consumables and contract detail.
Decrease in on-site maintenance time by monitoring the status of compressors deployed across regions and factories in real time.
Provides urgent service or repairs by detecting unusual operating conditions (temperature/pressure/current) and automatically sends email notifications to service centers, thus minimizing equipment downtime.
Gain insights on compressor operations by analyzing real-time data and trend charts

Use Case 2

Project Objective - To improve production capacity and avoid downtime, a global biotechnology manufacturing company implemented Seebo Predictive Analytics.

The Challenge - The company’s quarterly operations evaluation showed a 3.6% upsurge in downtime during production. The subsequent pipeline jams between the reactor and the centrifuge in the production line led to recurrent equipment cleaning events and slowdown during the batch production, high levels of waste, a decreased capacity, and lengthened time to market.

The Solution

Seebo assessed the past and online data from the production line and recognized the association of variables - exact differences in mixing duration, distillation time and reaction temperature, which were causing the obstruction.
Based on these findings, the Seebo solution could offer a prediction notification to the operational team before the blockage occurred again.
As a result of the Seebo Solution, the plant returned to anticipated production capacity and the production team was able to pinpoint the right predictive maintenance schedule.

Results

83% reduction in downtime instances
72% savings in downtime related costs
98% on time delivery rate
1% production capacity

What are the applications of Predictive Maintenance solution?

Manufacturing - The average manufacturing applications of predictive maintenance are many, right from managing equipment on the plant floor, to increasing operations and refining workers’ safety. This trend will possibly continue, particularly as the cost of sensing technology continues to drop, mainly due to the shift from wire-based sensors to wireless ones.
Utility Suppliers - Utility suppliers are applying predictive analytics to the big data produced by smart meters so they can identify early cautioning signs of supply and demand issues on the grid and resolve them before they lead to outages.
Insurance - The insurance industry will benefit from being able to make better use of predictive maintenance around the probability and effect of dangerous weather conditions. Supermarkets and their suppliers could enhance their operations based on more precise forecasts about crop yield and production.
Connected Car - Connected cars currently create and transmit large volumes of performance data from sensors spread across the vehicle. This data goes straight to manufacturers or car dealerships, who can then notify drivers of any problems that require servicing before they experience the issue of their car breaking down.

How to develop a Predictive Maintenance program?

Create a Strategy

Deploying a predictive maintenance system involves several hardware modifications, software changes, and adjustments to the manufacturing and maintenance culture.
The best way is to start small, with a concentrated pilot program. Select a single issue to address.

Decide the Right Asset to Test

Applying predictive maintenance to the assets that have failed earlier, particularly if they have failed frequently, saves on a lot of downtime as well as the costs associated.
Applying predictive maintenance to troubled assets allows maintenance and operations teams to carefully run the longer with the assurance that if condition begins to change, they can stop the asset before catastrophic failure takes place.

Predictive Maintenance Pilot Project

Predictive maintenance leverages several technologies to track the condition of rotating equipment. Each technology operates over a different interval. It’s critical for the teams to match the timeframe to the needs of the application.

Set a Response Procedure

While deploying predictive maintenance program, it is to critical to establish a process for answering to glitches.
If the plan is based on constant online condition tracking, the process is as simple as waiting further directions from the vibration tech or field technician.

Create a Data Analysis Plan

Before ascending to several assets, a data analysis plan should be created. This is critical for constant online tracking.
The data volumes are vast enough that they can consume large amounts of storage space and bandwidth.

Apply Predictive Maintenance to More Assets

Once proven, the teams may apply predictive maintenance to all the critical, troubled, difficult to source or difficult to replace assets.

Trends in Predictive Maintenance market

The automotive industry is as an early adopter of IIOT Predictive Maintenance. The industry is experiencing unsettling changes and has accepted its need to re-define business models and core offerings.
Conventional predictive maintenance (PdM) solutions were justified based on costs savings and other operational standards. With Industry 4.0, businesses are considering the impact on top-line revenue from their big-data investments.
Currently, industrial plants require predictive maintenance that provides automated repair schedules, inventory management and inspection. The holistic approach to asset maintenance is vital if companies want to gain all the value generated by big data.
By using machine learning with predictive maintenance, manufacturers can get a comprehensive view of their operations and leverage solutions that span multiple assets regardless of sensor type, asset class or age.