Predictive Maintenance: The Next Frontier in Manufacturing

In industries where equipment failure is one of the largest causes of downtime, manufacturers must find solutions that prevent lengthy and unplanned downtimes. Why? Unplanned downtime can destroy their ability to be competitive in the market. Traditional solutions to handle these downtimes are either replace the parts before their end of life comes up by making calculated guesses, or predict downtimes based on past occurrences. Unfortunately, both solutions are not a perfect fit and can be cost-intensive and very risky on the manufacturer’s end. The current trend of maintenance requires a tradeoff, whether it be reactive, planned, or proactive, whereas, in the predictive maintenance era, this will not be required.

Why Do traditional solutions have tradeoff?

Due to how limited traditional solutions are, they have quite a few tradeoffs when utilizing them. For instance, if a manufacturer wants to be reactive to downtimes, they must understand when their machine parts will hit maximum utilization. At max utilization, the whole machine can become damaged and result in the downtime.

On the other hand, if a manufacturer wants to plan for the downtime and not risk the breakdown of their machines, they must replace parts before their end of life, with the tradeoff being increased part replacement costs. To be proactive in protecting themselves from unplanned breakdowns, one must increase maintenance costs to extend machine part life. As we can see, these tradeoffs are not necessarily cost-effective or foolproof solutions. With predictive maintenance, these tradeoffs are eliminated as it minimizes downtime and backs up proactive maintenance with data so that costs can be maintained.

How does the Internet of Things work with predictive maintenance?

When you attach the Internet of Things to a physical asset like a manufacturing machine, you create a cycle between the IoT device and the physical machine. This cycle works on a physical to digital and back to physical cycle, meaning your physical asset provides digital data to the device, which creates a physical solution.

Here is how it would work.

• An IoT device with sensors is placed onto the manufacturing machine. The sensors generate data that pertains to how the machine is functioning.
• The generated data is digitized and analyzed for insights. This could be analyzing information about part breakdown percentages, parts that are not working correctly, or even data on the end of a specific part of the life cycle.
• These insights are relayed back to the machine and physical actions, like replacing parts can be taken.

Essentially, this IoT enabled cycle allows organizations to take actions based on real-time data, making companies more agile in their response times and creating smoother operations with minimized disruptions.

How does predictive maintenance ensure smooth operations?

While the major benefit of predictive maintenance is the ability to identify and manage risks that are associated with machine downtimes, it can be used across a wide variety of industries that require the assessment of operational data.

Here is how predictive maintenance ensures that a smooth operation occurs.

• By using a combination of science, math, and engineering principles, predictive maintenance brings dependability and efficiency to the productive process and all products within that process.
• Predictive maintenance can analyze multiple operational data sources and types. It can generate and analyze real-time data, batch-data, structured or unstructured data, and even at rest or streaming data components.
• By using data from IoT enabled devices, the predictive maintenance model can be continuously improved upon, factoring in other types of data generation points like motor circuits, vibrations, and thermography.
• Predictive maintenance can create a data-based multivariate metric that provides manufacturers with information on end of life or remaining life for parts. This is statistically significant because it removes the guesswork that is used with traditional methods.

Which industries can be impacted the most with Predictive maintenance?

1) Structural Infrastructure:

In countries where infrastructure is in dire need of repair, predictive maintenance can aid in helping construction crews know what needs to be repaired when and why. For instance, things like bridges, sewers, and highways that go unrepaired for long periods can have dire consequences on the population, but if a predictive maintenance model is used, disasters can be avoided, and costs controlled.

2) Automakers:

One always hears about the vehicle recalls, which is not only annoying for customers who have these vehicles, but it poses quite a dangerous risk to those who have vehicles being recalled for serious issues. If automakers were to use data and machine learning from predictive maintenance, issues could be uncovered during the design and roll-out phases, preventing major recalls.

3) Oil & Gas Production:

There are millions of pipelines that traverse countries with the sole purpose to transport oil and gas. Many of these pipelines were constructed decades ago, resulting in the deterioration from overuse and aged materials. As these pipelines get older, there is a much higher likelihood that failures will increase. Being able to predict and prepare for these situations can avert major disasters.

4) Airline Maintenance:

When it comes to airline maintenance, it can get complicated very quickly due to how vast the aviation industry is. It may take several teams to understand why a specific part is in disrepair. Predictive maintenance offers a safer and simpler solution that is economical.

Is Predictive Maintenance Possible?

Yes, predictive maintenance is technically possible, and it is currently technically accessible as well. Companies do not need to make massive investments into acquiring the predictive maintenance model because the data that is needed to be proactive about maintenance is already available. It just needs to be captured, stored, and analyzed. The first step in making it happen is connecting machinery to IoT sensors so that real-time data can be tracked. This data should then be fed into a network where it can be stored and processed in large volumes. Once the data is gathered, anomalies and patterns should be tracked, and corrective action should be taken based on data insights.

Let’s Nurture – the best IoT app development company provide expert consulting to help Companies not to make massive investments into acquiring predictive maintenance. Our expertise in providing IoT based solutions has helped us in developing a Smart Automotive Solution, KarConnect, on Web and Android platforms.

Let’s talk and build innovations around us!

TensorFlow: A Framework Companion for Machine Learning | AI-ML Solutions

Nowadays, Machine Learning has become a major component in large business operations that are utilizing the technological advancements of artificial intelligence. The term, ‘Machine Learning’ was coined back in 1959 by IBM but hasn’t seen any significant progression until 2016. ML is considered a subset of artificial intelligence, as it provides the systems needed for an AI to learn, improve upon, and apply what it experiences without explicit programming to tell it to do so. To put it simply, machine learning focuses on developing frameworks for computers to access data, use data, and learn from said data on their own. Although complex in its own right, machine learning frameworks such as Google’s TensorFlow, have simplified the process, refining down results, training models, and analytic predictions. This has caused a massive ripple across all industries that use information technology, including healthcare, automotive, gaming, and aviation to name a few.

About TensorFlow and its Purpose:

TensorFlow is an open-source machine learning framework or software library, created by the Google Brain team. It makes use of observation skills and reasoning skills through the combination of machine learning, deep learning, and efficiency algorithms that speed up the learning and application process. The framework has a seamless front-end API, courtesy of programming language Python, which is useful for building all sorts of applications, and it utilizes C++ (programming language) for high-performance purposes.

 

Although categorized as a software library, it is actually a set of APIs, that allow programmers to have full control over the models they build, meaning that they do not have to hand over low-level algorithms each time they want to use the codebase. Plus, TensorFlow works as a visual learning tool through TensorBoard, which gives programmers real-time visualizations of all machine learning work done within the framework.

What are some Key Highlights of TensorFlow?

TensorFlow allows programmers to exclusively focus in on the overall logic of the application they are building rather than having to deal with or program every micro detail into the application (this is a process called abstraction). This means less time is spent on incorporating multiple algorithms that result in one actionable function.

TensorFlow has an eager execution mode, which is a method that allows programmers to modify and evaluate each individual graph operation rather than requiring them to develop one whole graph as a single object. The TensorBoard visualization tool also allows programmers to profile and inspect these graphs in a similar way you would on an interactive dashboard, making it quite user-friendly.

The flexible nature of the TensorFlow architecture allows its computation to be deployed across a large variety of platforms. This can be used to make these platforms more efficient; allow developers to create in-browser incarnations for shared developmental models, and allow performance to be amplified.

TensorFlow brings machine learning to everyone as programmers can use it to build deep neural networks that can then be run across thousands of computers through data centers. Due to the fact that it is open-source software, industry sectors and associated companies can leverage TensorFlow for natural language processing, image recognition, and video/audio message scanning. Currently, TensorFlow is being widely used to prevent blindness in patients with diabetic retinopathy.

What Can TensorFlow Do Exactly?

Due to the fact that almost all modern living amenities are powered through data, such as Netflix’s recommendations list and Siri’s answers on the iPhone, major businesses and companies are using machine learning through TensorFlow to improve upon internal processes. Things like conversion rates are getting boosted as companies are now able to use predictive analysis to make more accurate decisions. Beyond this, companies are looking for new ways to enhance customer service by using chatbots, voice-activated devices, and text-based applications. TensorFlow impacts these by providing the prediction, perception, understanding, and classification require to make these types of applications work.

What Are The Real World Applications of TensorFlow?

• It can be used with voice and sound recognition based applications such as voice search, voice recognition in IoT, security, and automotive sectors and in sentiment analysis. More commonly known, TensorFlow algorithms (models) perform as customer service agents in voice-activated assistants like Google Now.
• It can be used in text-based applications for threat detection, sentimental analysis, emotion recognition, and fraud detection. Common examples of these include Smart replies, a popular framework that uses sequence-to-sequence learning to automatically generate email responses, and Google translate. Using neural networks Tensorflow can be very useful to develop conversational chatbot.
• It can be used for face recognition, photo clustering, machine vision, and image search applications often found in the aviation, automotive, and healthcare industries. These applications are commonly used to identify individuals or understand an object’s context. TensorFlow algorithms are being used in the healthcare industry to spot patterns, identify information, and scan diseases in humans.
• The TensorFlow Time Series uses algorithms to extra significant statistics, forecasting non-specific time periods. Common examples of this type of use are in Netflix’s customer analysis statistics that state how much time was spent on the platform across a specific time period and relate that to what a customer may like to watch in the future.
• TensorFlow can be used with real-time thread detection in gaming, at airports, and the security sector. An example of this would be the use of TensorFlow for video classification datasets as a way to accelerate the understanding of transfer learning, representation learning, and noisy data modeling.

TensorFlow is categorized as a second generation machine learning system and is currently being used by Google itself due to the wide-ranging capabilities and implications it has. Beyond making a ripple across the entire IT sector, TensorFlow is best known for its capability at handling unstructured data, dealing with large-scale problems, and conveying images with high accuracy. This is what makes this open-source library a must-have framework companion for companies who are adopting artificial intelligence, predictive analytics, and machine learning.

How Let’s Nurture can help for Machine Learning Development?

Let’s Nurture is the top custom mobile app development company providing AI-ML solutions to an array of industries. Our AI-ML engineers have developed multiple solutions like face detection, object detection, deep learning systems using Python, OpenCV, and Tensorflow. We also have expertise in custom AI-powered Chatbot development using Tensorflow and Dialogflow. Our AI programmer also has proficiency in AI engines like IBM Watson, Amazon Lex, Microsoft Luis, Facebook Wit, and Google AI. Let’s Nurture is also the best IoT app development company who provides custom IoT solutions using Tensorflow technology. We have developed in-house IoT based Smart urban farming solutions named AgriKonnect. Using Tensorflow, we can detect plants being affected by pest and diseases through IoT sensors and cameras.

We utilize machine learning, neural networks, and artificial intelligence to help businesses think, predict & act. If you wish to know more about what we can do with Tensorflow to provide innovative solutions, please get in touch with our Machine Learning experts.