Industrial big data is a new concept, literally understood, industrial big data refers to the big data generated in the application of industrial information.
With the in-depth integration of informatization and industrialization, information technology has penetrated into all links of the industrial chain of industrial enterprises, such as barcodes, QR codes, RFID, industrial sensors, industrial automatic control systems, industrial Internet of Things, ERP, CAD/CAM/CAE/ CAI and other technologies are widely used in industrial enterprises.
Especially with the application of new-generation information technologies such as the Internet, mobile Internet, and Internet of Things in the industrial field, industrial enterprises have also entered a new stage of development in the Internet industry, and the data held by industrial enterprises has become increasingly abundant.
The application of industrial big data will bring a new era of innovation and transformation in industrial enterprises. Through the low-cost perception, high-speed mobile connection, distributed computing and advanced analysis brought about by the Internet and mobile Internet of Things, information technology and global industrial systems are being deeply integrated, bringing profound changes to global industries, and innovating the R&D and production of enterprises. , Operation, marketing and management methods. Hangao Tech (SEKO Machinery) applies Internet technology to the control system of the intelligent stainless steel industrial welded pipe making machinery, so that the technical teams of both parties can monitor production data in real time, find faults during operation, and prevent shutdowns.
Therefore, the problems and challenges faced by industrial big data applications are not less than those of the Internet industry, and in some cases they are even more complicated.
These innovative industrial enterprises in different industries have brought faster speed, higher efficiency and higher insight.
Typical applications of industrial big data include product innovation, product fault diagnosis and prediction, industrial production line IoT analysis, industrial enterprise supply chain optimization, and product precision marketing. This article will sort out the application scenarios of industrial big data in manufacturing enterprises one by one.
1. Accelerate product innovation
The interaction and transaction behavior between customers and industrial enterprises will generate a large amount of data. Mining and analyzing these customer dynamic data can help customers participate in product demand analysis and product design innovation activities, and make contributions to product innovation.
Ford is an example in this regard. They applied big data technology to the product innovation and optimization of the Ford Focus electric car. This car has become a veritable "big data electric car." The first generation of Ford Focus electric vehicles generated a lot of data when driving and parking.
While driving, the driver continuously updates the vehicle's acceleration, braking, battery charging and location information. This is useful for drivers, but the data is also sent back to Ford engineers to understand the customer’s driving habits, including how, when and where to charge. Even if the vehicle is at a standstill, it will continue to transmit data on the tire pressure and battery system of the vehicle to the nearest smart phone.
This customer-centric big data application scenario has many benefits, because big data enables valuable new product innovation and collaboration methods. Drivers get useful and up-to-date information, while engineers in Detroit aggregate information about driving behavior to understand customers, develop product improvement plans, and implement new product innovations.
Moreover, power companies and other third-party suppliers can analyze millions of miles of driving data to determine where to build new charging stations and how to prevent the fragile grid from overloading.
2. Product fault diagnosis and prediction
This can be used for product after-sales service and product improvement. The introduction of ubiquitous sensors and Internet technology has made real-time diagnosis of product faults a reality, while big data applications, modeling and simulation technologies have made it possible to predict dynamics.
During the search for the lost connection of Malaysia Airlines MH370, the engine operating data obtained by Boeing played a key role in determining the path of the lost connection of the aircraft. Let's take the Boeing aircraft system as a case to see how big data applications play a role in product fault diagnosis.
On Boeing’s aircraft, hundreds of variables, such as engines, fuel systems, hydraulics, and electrical systems, make up the in-flight state. These data are measured and sent in less than a few microseconds. Taking the Boeing 737 as an example, the engine can generate 10 terabytes of data every 30 minutes in flight.
These data are not only engineering telemetry data that can be analyzed at a certain point in the future, but also promote real-time adaptive control, fuel usage, component failure prediction and pilot notification, which can effectively achieve fault diagnosis and prediction.
Let’s look at an example of General Electric (GE). The GE Energy Monitoring and Diagnostics (M&D) Center in Atlanta, USA, collects data on thousands of GE gas turbines in more than 50 countries around the world, and can collect 10G data for customers every day. Analyze the constant big data flow from the sensor vibration and temperature signals in the system. These big data analysis will provide support for GE's gas turbine fault diagnosis and early warning.
Wind turbine manufacturer Vestas also improved the layout of wind turbines by cross-analyzing weather data and its turbine meter data, thereby increasing the power output level of wind turbines and extending service life.
3. Big data application of industrial IoT production line
Modern industrial manufacturing production lines are equipped with thousands of small sensors to detect temperature, pressure, heat, vibration and noise.
Because data is collected every few seconds, many forms of analysis can be realized by using these data, including equipment diagnosis, power consumption analysis, energy consumption analysis, quality accident analysis (including violations of production regulations, component failures), etc.
First of all, in terms of production process improvement, using these big data in the production process can analyze the entire production process and understand how each link is executed. Once a certain process deviates from the standard process, an alarm signal will be generated, errors or bottlenecks can be found more quickly, and the problem can be solved more easily.
Using big data technology, it is also possible to establish virtual models of the production process of industrial products, simulate and optimize the production process. When all process and performance data can be reconstructed in the system, this transparency will help manufacturers improve their production processes.
For another example, in terms of energy consumption analysis, the use of sensors to centrally monitor all production processes during the equipment production process can find abnormalities or peaks in energy consumption, so that energy consumption can be optimized during the production process and all processes can be performed. Analysis will greatly reduce energy consumption.
4. Analysis and optimization of industrial supply chain
At present, big data analysis is already an important means for many e-commerce companies to enhance the competitiveness of their supply chains.
For example, the e-commerce company Jingdong Mall uses big data to analyze and predict the demand for goods in various places in advance, thereby improving the efficiency of distribution and warehousing, and ensuring the customer experience of the next day.
RFID and other product electronic identification technology, Internet of Things technology, and mobile Internet technology can help industrial enterprises obtain big data of a complete product supply chain. Using these data for analysis will bring about a significant increase in warehousing, distribution, and sales efficiency and a significant cost. decline.
There are more than 1,000 large OEM suppliers in the United States, providing more than 10,000 different products to manufacturing companies. Each manufacturer relies on market forecasts and other different variables, such as sales data, market information, exhibitions, news, and competitor data , And even weather forecasts to sell their products.
Using sales data, product sensor data, and data from supplier databases, industrial manufacturing companies can accurately predict demand in different regions of the world.
Since inventory and sales prices can be tracked, and can be bought when prices fall, manufacturing companies can save a lot of costs.
If you reuse the data generated by the sensors in the product to know what is wrong with the product and where parts are needed, they can also predict where and when parts are needed. This will greatly reduce inventory and optimize the supply chain.
