What Is Industry 4.0

The evolution of Industry 4.0 traces back to the early 21st century, marked by the convergence of digital solutions with traditional industrial processes. Initially coined in Germany, this Fourth Industrial Revolution has progressively matured, influencing diverse sectors globally.

At its core, Industry 4.0 is a comprehensive framework that combines the newest technologies to upgrade industrial processes and production. These technologies include:

• Internet of Things
• Cloud computing
• Artificial intelligence and machine learning
• Edge computing
• Cybersecurity
• Augmented reality and virtual reality
• Digital twins
• Additive manufacturing
• Robotics

Industry 4.0 represents the amalgamation of these smart manufacturing technologies, creating a holistic and interconnected ecosystem where machines, systems, and humans collaborate seamlessly. This integration fosters a highly adaptable and intelligent industrial environment.

The first industrial revolution began in the late 18th century with the mechanization of the textile industry, Nearly a century later, in the second half of the 20th century, the third industrial revolution appeared with the emergence of computers and the beginnings of automation, when robots and machines began to replace human workers on the assembly lines.

And now, we are in the fourth industrial revolution called “Industry 4.0,” – which is the next phase in the digitization of the manufacturing industry:

• Staggering rise in data volumes, computational power, and connectivity
• Evolution of advanced analytics and machine learning capabilities
• Emergence of new technologies such as touch interfaces and augmented-reality systems that enable the interaction between humans and machines
• Improvements in transferring digital instructions to the physical world, such as 3-d printing and advanced robotics.

Industry 4.0 is rapidly shifting manufacturing from isolated, optimized cells of business processes, systems, and resources to fully integrated data and product flows across corporate borders.

Therefore, manufacturing companies have to build up capabilities in IoT service development and operation. Put differently, the achievement of “integrated production for integrated products”.

A lot of these manufacturing firms will find this difficult, because it is not in their DNA. It is also not about developing additional IT skills. To make the change possible, the value propositions of these companies should evolve, this means a change in almost all parts of the organization, from engineering to sales right through to aftermarket services. Predictive maintenance (PdM) techniques are designed to help determine the condition of in-operation equipment or machinery and predict when maintenance should be performed

Top IoT Applications in Industry 4.0

In the context of Industry 4.0, several IoT application technologies are pivotal for transforming traditional industrial practices into smart, interconnected systems. Here are the top 10 IoT application technologies driving Industry 4.0:

• Sensors and Actuators

Sensors collect data from the physical environment (e.g., temperature, pressure, vibration), while actuators perform actions based on commands (e.g., adjusting valves, moving machinery). This enables real-time monitoring and control of industrial processes by providing essential data and feedback.

• Edge Computing

Edge computing involves processing data closer to the source of data generation (e.g., sensors, machines) rather than relying solely on centralized cloud servers. This reduces latency, enhances real-time decision-making, and minimizes the amount of data sent to the cloud, which can improve response times and reduce bandwidth usage.

• Machine Learning and Artificial Intelligence (AI)

AI and machine learning algorithms analyze large volumes of data to identify patterns, make predictions, and automate decision-making. It facilitates predictive maintenance, quality control, and process optimization by leveraging data-driven insights.

• Cloud Computing

Cloud computing provides scalable and flexible computing resources over the internet, enabling storage, processing, and analysis of vast amounts of data. This supports data aggregation, remote access, and advanced analytics, which are essential for managing and analyzing IoT data at scale.

• Digital Twins

Digital twins are virtual models of physical assets, processes, or systems that simulate real-world conditions using IoT data. Also enables real-time monitoring, simulation, and optimization of physical entities and processes, leading to improved design and operational efficiency.The most well-known application of these frameworks is to conduct simulations designed to identify vulnerabilities and opportunities for improvement. They can also be employed to perform a preliminary assessment of how a system or machine would behave under specific conditions.

• Industrial IoT (IIoT) Platforms

IIoT platforms integrate various IoT technologies and provide a unified environment for managing devices, data, and applications. Offer comprehensive solutions for device management, data analytics, and application development, facilitating seamless deployment and operation of IoT systems.

• Robotics

 Robotics is extensively used to perform repetitive and high-precision tasks across various production lines. Robots handle hazardous tasks, decreasing the risk of work-related injuries. Advanced robotics improve efficiency and productivity through rapid, autonomous operation. Modern robotics have advanced to the point where they no longer require human supervision.

• Predictive Maintenance

Predictive maintenance is the ability of the system to predict a machine's failure. Predictive maintenance can reduce or even abolish unplanned downtime by predicting when a machine needs checkups or when a machine will become faulty. Predictive maintenance is one of the most widely discussed use cases in the IoT ecosystem.

• Connected Cars

From a hardware perspective, the car can connect to the internet, by plugging in devices to the OBD2 port of the car to extract the vehicle data. From a software perspective, here are some connected car use cases:

In this blog post, I will walk you through the two most widely discussed use cases in industry 4.0 - predictive maintenance and connected cars.

Predictive maintenance(PdM) techniques are designed to help determine the condition of an in-operation equipment/machine to predict when maintenance should be performed.

The main promise of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected machine failures.

You would have read many articles stating that predictive maintenance is the same or a part of condition-based maintenance. Based on our experience, we chose to call it out separately as the scope and implementations are quite different.

Condition-based maintenance primarily uses rules and anomaly detection techniques to predict an outcome. On the other hand, predictive maintenance analyzes volumes of historical data, trends, correlations, trends, and machine specifications to anticipate an outcome.

Predictive Maintenance

Predictive maintenance is the ability of the system to predict a machine's failure.Predictive maintenance (PdM) can reduce or even abolish unplanned downtime by predicting when a machine needs checkups or when it will become faulty. Predictive maintenance is one of the most widely discussed use cases in the IoT ecosystem.

Leveraging IoT for Predictive Maintenance in Asset Lifecycle Management

A simple use case would be to predict the remaining life cycle of an asset and when maintenance is required using the streams of data we get from the asset, such as the actual ‘wear and tear’ data of its parts. Imagine a dashboard that lists down the assets and their metadata, like manufacturing date, installed date, type, etc., along with their actual usage. It also depicts external factors, predictions on the remaining life cycle of the asset, and a maintenance date. These factors can be used to plan minimum maintenance downtime, schedule spare parts delivery, and ensure maintenance is executed with the least impact. Also, most manufacturers typically have historical maintenance records of the systems and usage dates in some form – this can be a valuable input to predict maintenance activity.

Let’s take an example of a leading elevator manufacturing company that supplies elevators across the globe.

Can they use an IoT system to predict when their elevator lift cables should be replaced? Manufacturing innovations are happening in elevator cables, like using super-light carbon fibre ropes that increases the lifespan of the cables. Changing the lift cables is an expensive maintenance activity, and its failure can have significant downtime.

Factors such as the availability of new lift cables, specialized technicians, and compliance checks can have a considerable impact on business operations.

To carry out any predictive maintenance for elevator lift cables, the manufacturer needs to decide the data points that would be required to predict the failure.

As part of its product design, the manufacturer probably installed a sensor to track the running time or distance served by the cable, a sensor to detect if the elevator is descending faster than its designated speed, start and stop instances of the elevators. Sensor input, together with the cable’s specified life expectancy, can be used to predict when the lift cables need to be replaced.

The role of machine learning in predictive maintenance

Predictive maintenance involves building out machine learning models based on volumes of data. Developing machine learning models requires considerable time and effort. It’s virtually impossible to expect a system to devise a predictive model that is 100% accurate (not even humans operate with that level of accuracy), but it should be considerable enough to suggest a cause of possible failure with reasonable accuracy.

Open-source scalable machine learning models like Spark MLlib or commercial offerings like SPSS from IBM or Azure Machine Learning for Microsoft can aid in building predictive models.  The traditional predictive maintenance machine learning models like Support Vector Machines, Logistic Regression, and Decision Trees are based on feature engineering, which is the manual construction of correct features( attributes) using domain expertise and similar methods. This actually makes it hard to reuse the model as feature engineering is specific to the problem scenario.

Using deep learning algorithms, we can automatically extract the right features from the data, eliminating the need for manual feature engineering. Among the deep learning networks, Long Short TermMemory (LSTM) networks are used in the predictive maintenance domain since they are very good at analyzing huge volumes of time series sensor data.

The model once developed, can be integrated into your IoT platform to predict outcomes in real time.

How companies are implementing predictive maintenance solutions

Deutsche Bahn and Siemens have launched a 12 month pilot project to provide data to support predictive maintenance of the Class 407 Velaro D high-speed train fleet.Data received from the operational Velaro D fleets will be consistently analyzed at the Siemens mobility centre in Munich, supplementing diagnostic data available onboard. This will help the maintenance team identify the impending faults and malfunctions, as well as the sources of these problems, early on. Specialist technicians will then recommend corrective actions to the technicians at the Deutsche Bahn’s workshop.“The objective of the pilot project is to the precisely align maintenance work with the vehicle’s actual status. With intelligent algorithms and precise analytics, availability is increased,” said Jochen Eickholt, CEO of the Siemens Mobility Division.Another good example of predictive maintenance is ThyssenKrupp. ThyssenKrupp partnered with CGI and Microsoft Azure to send alerts when their elevators need repairs. Predictive maintenance system sends alerts when an elevator is about to go out of function and even teaches the technicians the areas of error.

Connected Cars

Before we deep dive into the connected car use case, let’s first start with a formal definition of a connected car. As per Wikipedia – “A connected car is a car that is equipped with internet access, and usually also with a wireless local area network. This allows the car to share internet access with other devices both inside and outside the vehicle. ”From a hardware perspective, the car can connect to the internet by plugging in devices to the OBD2 port of the car to extract the vehicle data. Now a days, insurance companies are issuing dongles – devices that plug directly into the OBD2 port and connect wirelessly to a network – to customers as a way to achieve discounts. This generally involves using data pulled from the car’s OBD2 connection to analyze driving habits and award a discount for low-risk behaviour.  Allstate’s Drivewise program, for example, looks at speed, how quickly the driver applies brakes, the number of miles driven, and when a person drives.

Interesting connected car use cases

From a software perspective, here are some connected car use cases:

Location Tracking

Real-time performance monitoring of the car

Condition-based maintenance

Predictive maintenance

Driver assistance

Behaviour analysis of the driver

Recommendation based on driving patterns

Speak on

The above use cases apply to both individuals and fleet management firms that could monitor cabs remotely, create high-speed alerts, analyze drivers' behaviour patterns, Street-view (Google or similar), wait time and actual distance covered. Overview of the connected car ecosystem and related services Connected Car Ecosystem: Services and Data Utilization The above use cases are developed and deployed as cloud services. Primarily, the OBD2 data and GPS location is made available continuously to the core IoT platform. Let’s inspect some of the information available as part of the OBD port. The values can be retrieved by using the PIDs (parameter Ids) codes from OBD2 port. For instance, to retrieve the engine speed, thestandard PID 12 code needs to be used. Some of the information that can be retrieved are:

• Vehicle speed
• Engine RPM
• Coolant temperature
• Air flow rate
• Absolute throttle position
• Absolute load value
• Fuel status
• Fuel pressure
• Battery voltage

The above is only a minimal set of data from the car. Your car is already equipped with hundreds of sensors, and with the car now connected, the data generated through the car is being utilized for various other use cases.

Once the OBD and GPS data are available on the IoT core platform, the platform can start consuming them. The IoT core platform would read the continuous stream of vehicle data, uncompress the same, persist and analyze the data, execute the required cloud services (condition monitoring, custom alerts, behaviour analysis) and notify the user. A mobile or web-based application is provided to the user to view the data in real time offered by various services.

Given below is the set of use cases with brief implementation details:

• Location Tracking: Provides a map view and location of the car on a mobile or web application.
• Real-time Performance Monitoring of the Car: Offers a real-time graphical and tabular view of the car's performance data from the OBD port,
• Condition-based Maintenance: Applies the same approach discussed earlier to track performance, detect anomalies (deviations from normal), and provide alerts if maintenance is required.
• Predictive Maintenance: Utilizes various approaches discussed earlier to predict maintenance needs. For example, Mahindra Reva's electric micro-car with built-in connectivity provides real-time insights into car status and performance, enabling proactive maintenance and support.
• Driver Assistance: Provides various aids to the driver, such as route planning to minimize fuel consumption or suggesting alternative routes based on weather conditions.
• Driver Behavior Analysis: Classifies the driver’s ability over time using vehicle data (e.g., speed, RPM, throttle position), idling, accelerometer (tracking rapid lane changes), brake pressure, accelerator pedal position, GPS data (calculated vehicle speed), and driver history. These parameters feed into a machine learning model that classifies the driver as novice, unsafe, neutral, assertive, or aggressive.
• Recommendations based on Driving Patterns: Analyzes driving patterns over time to recommend optimal driving techniques, such as fuel-saving strategies (e.g., maintaining a constant speed in second gear in geared cars), reducing brake usage, easing off the accelerator during speed bumps, avoiding sharp turns, and yielding at specific locations (tracked via GPS, accelerometer, and steering angles).
• Natural Language Interaction: Enables users to converse with their car in natural language, accessing features and receiving recommendations based on the collected data—an intelligent system similar to Siri that comprehensively understands the car's status and contextually provides effective responses.