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Predictive Maintenance: what it is and how is it transforming manufacturing?
Predictive maintenance uses real-time data collected via the Industrial Internet of Things (IIoT) platforms to continuously analyze the machine's condition as it operates normally so that any sudden and unexpected failure can be avoided. It allows organizations to monitor and check the status of indicators like lubricants, speed, and bearing speed. These tools detect any abnormal behavior when the machine is operating normally and immediately send the report to the owner of the machine so that any potential incident can be avoided. The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.
In simpler terms, predictive maintenance analyzes and monitors the health of an asset in real-time and reports abnormalities to the owner in real-time to avoid any potential failure, breakdowns, and so on. It also offers the manufacturer an option to plan maintenance as per their production schedule. agraph
Working of predictive maintenance
Predictive maintenance works in the following ways:
- Digital status analytics: Mining of machine digital repositories, understanding the meaning of statuses, and using those together with Machine learning, digital rein, and Artificial Intelligence to predict specific failures. This is specificity important for more expensive, critical, and complex machines.
- Infrared analysis: Infrared analysis uses the temperature of the machine as an indicator of the problem. Abnormalities related to airflow, cooling, and motor stress can be identified using this method.
- Vibration analysis: Vibration analysis is one of the most common predictive maintenance methods used in plants with rotating machinery. Abnormalities related to balance, alignment, loose parts, etc can be detected via this method.
- Sonic acoustical analysis: This method uses sound converted into auditory or visual signals to detect problems like under-lubrication in both low and high rotating machinery.
How is predictive maintenance different from preventive maintenance?
- Preventive maintenance:
It occurs at regular time intervals depending on the lifecycle of the machine, irrespective of its usage, to prevent any issues.
- Predictive maintenance:
It occurs only as needed based on the reports provided by the IoT sensors regarding the status of the machine. This allows manufacturers to schedule maintenance to avoid any sudden unexpected potential breakdowns or failures.
How is predictive maintenance different from condition-based maintenance?
- Condition-based maintenance
This type of proactive maintenance uses sensors to collect real-time insights about a piece of machine based on various factors like temperature, vibration, and pressure. In this case, the service is only deployed when the machine conditions demand. This type of maintenance includes a risk of multiple machines needing service simultaneously.
- Predictive maintenance
Predictive maintenance is a type of condition-based maintenance, but it uses IIoT sensors at a larger scale to gain continuous insights into the machine's condition. Along with the equipment condition insights, it also uses big data methodologies to predict the machine degradation depending on the history of the equipment. It allows technicians to detect potential issues beforehand to avoid any potential issues and schedule maintenance more efficiently.
Key benefits of predictive maintenance
Here are the top five benefits predictive maintenance offers your organization:
- Minimized downtime
Predictive maintenance alerts technicians of any potential issues in advance so that they can take action upon them beforehand and avoid any unexpected failure of breakdowns. This reduces the downtime by as much as 30%, avoids disruptions that damage brand reputation, and allows you to schedule multiple service procedures.
- Better productivity
As there are minimal disruptions due to unexpected malfunction or breakdown, the productivity is optimized. The service time-to-resolution is 83% faster, no productive lags are there, maximum uptime is achieved, and asset usage improves.
- Lower field service costs
Pre-planned maintenance saves major costs to service departments by minimizing service truck rolls, better first-time fix rates, and optimized maintenance costs.
- Improved product design
The equipment data collected via the IIoT sensors allows product designers to increase equipment life spans, enhance product durability and reliability, and build more efficient machines in the future.
- Enhanced worker safety
By predicting and resolving the chances of an unexpected potential failure, you can ensure that no employees are working near machinery that is at risk of a potential malfunction. The service to the machine can be provided before it becomes hazardous..
Implementing predictive maintenance
Follow these steps to start with your predictive maintenance. There are several ways to do it:
Option 1: Do it yourself:
Program design
- Acquire permissions from your senior management
- Outline the advantages of the predictive maintenance
- List out equipment with a history of high failure rates along with its causes
IIoT installation
You can use machines having sensors, connected to an IIoT platform to conduct predictive maintenance.
System integration
Use IIoT tools to implement predictive maintenance. The connectivity established by IIoT based condition monitoring can be used to enhance efficiency using analytics, automation, and integration between OT and IT.
Schedule maintenance
Your team is equipped with advanced real-time alerts about equipment insights for scheduling service delivery and coordinating maintenance.
Option 2: accelerate with the combined platform and data science:
- Factory and machine survey: analyze all sources of data and identify critical ones
- Onboard machines: use iiot platform with already support for the functionality: from data collection to prediction.
- Focus on what’s important: data science and modeling: Avoid expensive timely and risky data integration: the platform will make data concerted for information to your data scientist or subject matter expert.
- Deploy and periodically train: deploy the model and utilize the platform user interface, alarms, and dashboard to manage your predictive Maintenance operations.
Use predictive maintenance and stand apart from the competition
The ultimate and numerous benefits of
predictive maintenance will provide you an edge over your competitors. Don’t face the risk of downtime and its associated hurdles. Be proactive with your plan and service delivery using predictive maintenance.
SELF SERVICE DATA STUDIO CASE STUDY This case study showcases GEMBO, a leader in SaaS Industrial IoT Platforms, enhancing a global manufacturer's operations by deploying its Precare Data Studio BI product across 70 machines. The solution, focused on self-service data management and analytics, resulted in significant manpower savings, improved self-service efficiency, and automated analytics distribution. Unique to GEMBO Precare is its ability to deploy independent sensors, connecting any machine to its cloud, thereby offering predictive maintenance and operational insights without substantial capital expenditure, distinguishing it from other market solutions.
GEMBO ( www.gembo.co ) is a leading innovator in the world of SaaS Industrial IoT Platforms, with a proven track record of delivering cutting-edge solutions that help customers achieve their business goals. The company's platform uses a combination of IoT, machine learning, and AI to provide customers with real-time insights into their operations, enabling them to make better decisions and optimize their performance. GEMBO is committed to continuous innovation, and the company is constantly looking for new ways to use technology to help its customers succeed. Customer Intro The customer is a multi $B Tier one manufacturer, with multiple factories around the globe. Manufacturing lines vary between electronic manufacturing to semiconductor, serving a variety of markets including Automotive, Networking, Industrial, Audio and Gaming. GEM has deployed Precare Cloud , Precare Edge , OEE Availability and predictive analytics Package . Its footprint grew from a few machines to over 90 machines in a few of their factories. Problem The customer has been utilizing OEE Availability analytics to identify machines with low availability and assigning them to responsible technicians. The customer's management team has been overwhelmed with emails from technicians providing status updates on the machines after they have taken corrective actions. The customer intends to streamline the process of monitoring machine status from the receipt of OEE Availability status to the resolution of availability issues, enabling the machines to operate efficiently based on established availability KPIs. The customer has provided the following workflow to illustrate their requirements.
GEMBO ( www.gembo.co ) is a leading innovator in the world of SaaS Industrial IoT Platforms, with a proven track record of delivering cutting-edge solutions that help customers achieve their business goals. The company's platform uses a combination of IoT, machine learning, and AI to provide customers with real-time insights into their operations, enabling them to make better decisions and optimize their performance. GEMBO is committed to continuous innovation, and the company is constantly looking for new ways to use technology to help its customers succeed. Customer Intro The customer is a Tier 1 multi-billion dollar global manufacturing leader in the power, discrete semiconductor, and passive electronic components (PES) space. GEMBO has deployed a suite of innovative, cloud-based solutions to the customer, including Precare Cloud, Precare Edge, OEE Availability, Performance, and Quality packages. These solutions have been deployed to 70 machines across two factory floors, and the customer intends to scale the deployment across their entire Asia-Pacific footprint. Problem The customer's equipment engineering team has been managing the productivity of their machines through manual Excel reports. These reports are prepared by machine operators, checked and consolidated by supervisors, and then reported to management by factory floor managers. The customer needs to minimize or eliminate these manual reports to improve the accuracy of the reports and save on manpower costs. The following are examples of manual reports that the customer needs to eliminate: 1. Calculation of the share of subcategories of Performance and Quality 2. Calculation of the percentage of Planned Downtime to Total Run Time 3. Graphical presentation of OEE Availability with Time Frame These reports are time-consuming and error-prone. They also require a significant amount of manpower to create and maintain. By eliminating these manual reports, the customer can improve the accuracy of their productivity data and save on manpower costs. Solution After a technical discussion with the customer, GEM integrated the manual Excel reports into the OEE analytics framework. The team then implemented the following process: Define the customer requirements Gather the excel reports Create an output image showing changes in the analytics Define data to be collected Collect data from the customers Collect the data from customer Validate the completeness of the data based on the requirements Collect missing data Confirm with customer if all data are accurate and structured properly based on their requirements Prepare the design Overview Customer Requirements Use Case System Diagram Code Diagram Review and approval of the design Testing on Developer Environment Deployment to Production Customer acceptance Results The migration of previously manually prepared OEE reports to a digital format using OEE Analytics resulted in the following benefits: 100% savings on manpower costs, as the reports are now generated automatically and do not require manual input. 100% accuracy, as the data is collected directly from the machines and is not subject to human error. 100% time savings for management, as they no longer need to spend time reviewing and validating the reports. Improved decision-making, as the Operations Group can now access real-time data and insights to make better decisions. Increased efficiency, as the Operations Group can now focus on core tasks and not on data entry and reporting. Conclusions GEMBO Precare has a set of powerful data acquisition, analytics, predictive and OEE tools for manufacturing equipment. GEMBO Precare compiles critical KPIs that can be used to easily trace back which machine or machine subsystem is responsible for a low KPI score. GEMBO Precare is also able to make predictions at equal or better than humanly possible for machine maintenance to be scheduled before a failure occurs. But most importantly, unlike other market solutions, GEMBO Precare is able to deploy its own sensors independently from any machine controller and fully connect any machine data island to the GEMBO Precare Cloud at a fraction of the cost of a new machine; hence, saving manufacturers from having to make large and risky CAPEX and OPEX investments for new machines. Contact Us Visit us at www.gembo.co or contact us via email at sales@gembo.co
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INTRODUCTION The GEMBO PRECARE platform enables manufacturers to seamlessly make the transition to Industry 4.0 literally overnight, while keeping their investments in legacy machines; thus, being able to tap into the potential of real-time access to machine data in order to improve efficiency, productivity and quality. Real-time access to machine data can be through any combination of sensors, serial and parallel port interfaces, Ethernet and sensors, no matter where the data is originating from, i.e., directly from the machine, or indirectly from a PLC or a data store on a network. Complex event process control at the edge of the cloud or at the machine itself requires hardware platforms with adequate processing power. GEMBO Precare supports edge processing through the use of powerful software agents. These agents take full advantage of the capabilities of the underlying hardware. In this white paper we show how the GEMBO Precare Industry 4.0 IoT platform takes advantage of Intel® x86 processor and Intel® Altera® Cyclone V SoC FPGA hardware capabilities to implement process monitoring and control at the edge in an industrial flow control application. GEMBO PRECARE MOTOR CONTROL AGENT Motors used in machines and production processes are very common and the ability to monitor them in real-time is understandably of crucial importance. GEMBO has developed an end-to-end motor control and monitoring solution in collaboration with Intel® and B&R Automation®, which takes advantage of the versatile and powerful GEMBO Agents for CEP at the edge and the GEMBO Precare cloud platform for monitoring and predictive maintenance. The process diagram shown below depicts a flow control application example. Control of a pump controls the flow rate in a pipe, and a safety valve prevents overpressure inside the pipe.
The Customer Semiconductor manufacturers are constantly under pressure to squeeze the most out of their capital and operational expenses, while at the same time facing tremendous pressure in a global market from their customers to deliver the highest quality within the shortest lead time. Whereas COOs are held responsible for CapEx, OpEx and overall performance across all manufacturing sites and business units, VPs at each business unit are responsible for CapEx, OpEx and overall performance of their own business unit, while factory floor directors are responsible for manufacturing operations at their plants to run as efficiently and productive as possible. In concrete terms this means minimizing unplanned machine downtime and maximizing machine throughput, while keeping manufacturing quality high; or in industry parlance, maximizing OEE (Overall Equipment Effectiveness). The Challenge Maximizing OEE in semiconductor manufacturing is a non-trivial task. The manufacturing workflow involves multiple stages; each handled by machines specialized for the particular operations involved in a stage. It is not uncommon to find machines from different OEMs handling the same or different tasks on the manufacturing floor, where some machines may be controlled by older generation Operating Systems (some with none), PLCs (Programmable Logic Controllers) that don’t have the ability to provide machine status, performance, quality and availability data in real-time to spot trends to predict when maintenance needs to be scheduled and optimize equipment effectiveness. This situation includes having no access at all to data for analysis and having access, but having to import the data in batch mode, missing crucial data. In addition, where the data is available, it is often in formats that differ from machine to machine, and more often than not, in formats incompatible with prevalent IIoT formats, such as JSON, XML, etc. To top it all off, a plethora of different communication protocols, ranging from SECS/GEMBO , SCADA and RS232/485 to various flavors of Industrial Ethernet compound the problem of access to data for analytics even further. As a consequence all stakeholders, from factory floor to corporate office, don’t have access to crucial data to help them make well-informed decisions towards OEE optimization in order to meet the end goal of growing bottom line revenue and market share. The Solution Industry 4.0 augments Industry 3.0 process control automation systems with big data and big data analytics. At one end of the Industry 4.0 spectrum this means real-time machine data access, while at the other end of the spectrum this means bridging the chasm between operational technology systems on the factory floor and information technology systems in the back-office. Industry 4.0 creates a digital twin or cyber physical system (CPS) of each machine on the factory floor. These digital twins allow all stakeholders to take advantage of the full Industry 4.0 spectrum of big data analytics opportunities. A 2018 PwC survey of the electronics manufacturing industry estimates that 45% of manufacturers are already deploying or are in process of deploying Industry 4.0 systems, while another 32% is planning to deploy Industry 4.0 systems within the next 5 years. The GEMBO PRECARE platform is able to offer semiconductor manufacturers a complete Industry 4.0 upgrade which is tailored to their specific needs, with all the benefits of big data analytics to optimize OEE, but without the cost of overhauling their existing machinery. The highlights of this solution are summarized below.