Asset maintenance management is the process of ensuring that the assets—machinery and equipment, fixtures, tools, infrastructure, and utilities—in a manufacturing plant are properly maintained in order to maximize their lifespan and productivity. This is because equipment churn for long hours, following a predefined process, in the production of goods/products that the company then ships to the customer's site or location. Timely maintenance of these assets is crucial to keep them running and improve their lifespan. In this article, we’ll use the terms asset and machine interchangeably for ease of understanding.
Let us go through an example to get additional context. Consider the example of a textile company by looking at its stages:
Fiber Preparation: Bale breakers, openers, and carding machines process raw fibers.
Spinning: Spinning frames create yarns from the prepared fibers.
Weaving or Knitting: Weaving machines create woven fabrics while knitting machines produce knitted fabrics.
Dyeing and Printing: Dyeing machines apply color; printing machines add patterns to fabrics.
Finishing: Machines treat fabrics to enhance appearance and performance.
Cutting and Sewing: Automated cutting machines cut fabric panels; sewing machines assemble garments.
Quality Control: Automated inspection systems check fabrics and garments for defects.
Packaging and Distribution: Finished textile products are packaged for distribution.
The machines running in tandem, for long hours under varying external conditions such as temperature, pressure, air quality, and humidity can face the following issues:
Power consumption spikes
Anomalous yarn tensions
Yarn breakage and subsequent replacement
These issues can cause the asset (here the machines) to slow down, deteriorate in performance, and break down. When machines work in a streamlined fashion, even the non-functioning of one machine in the lot can cause production to halt. This “ripple effect” can cause a plethora of other issues, avalanching into:
Production schedule downtime: This can cause schedule disruptions, affecting the overall production timeline. This can affect customer/client timelines.
Resource reallocation: To compensate for delays, resources such as labor, materials, and machinery need to be reallocated. This can result in long work hours and increase the complexity of resource allocation.
Inventory management: Production delays can cause imbalances in inventory levels, leading to excess inventory of some products and shortages of others. This can cause stock-out and overstocking issues.
Communication challenges: Delays in production can create communication challenges between different teams and departments, impacting coordination and collaboration.
Whether you’re a manufacturing manager, facility head, or production head, irrespective of the manufacturing domain, these are some issues that you can face as part of your daily operations. With this knowledge, we’re ready for our next phase. Below is a list of questions that we’ll address:
We’re going to go through each of these questions with relevant examples to help you understand better! For the sake of simplicity and uniformity, we’re going to stick to our above example of a textile company.
Asset maintenance management is the strategic process of planning, executing, and optimizing maintenance activities for an organization's physical assets. Maintenance is necessary for the asset to function as per its capacity. It also helps increase the asset’s lifespan. The key components of asset maintenance management include:
Maintenance planning: Developing comprehensive maintenance plans that outline scheduled maintenance tasks, intervals, and procedures for each asset.
Work order management: Generating and managing work orders for maintenance tasks, including assigning tasks to technicians, tracking progress, and ensuring timely completion.
Asset condition monitoring: Utilizing various monitoring techniques and sensors to assess asset health and performance, enabling proactive maintenance, and reducing the risk of unexpected breakdowns.
Resource allocation: Allocating the necessary resources, such as skilled technicians, equipment, and tools, to carry out maintenance tasks efficiently.
Cost optimization: Managing maintenance costs with asset performance and overall business objectives to achieve a cost-effective maintenance approach.
Documentation and reporting: Keeping detailed records of maintenance activities, including maintenance history, costs, and performance data.
Machine monitoring solutions or commonly referred to as asset performance management software are responsible for monitoring a machine or “listening” to it, to understand its health, performance, potential issues, and opportunities for improvement. Some of its features include:
Real-time monitoring: Offers real-time tracking of machine parameters and then compares this against thresholds to detect abnormalities and deviations. This helps in estimating reliability and developing proactive strategies.
Workflow optimization: Streamlines processes, and improves efficiency by digitizing assets, and providing accessibility to and analyzing real-time machine data. This helps optimize the overall workflow.
Improvement of safety conditions: Reduces major equipment repairs, enhances asset management, and minimizes downtime through early detection of performance issues, mitigating safety risks and potential hazards.
Improvement of equipment reliability: Enables condition-based maintenance, predicts potential failures based on historical data, and minimizes unexpected breakdowns, leading to enhanced equipment reliability.
The above features provided by a machine monitoring solution help increase an asset’s lifespan, allowing it to run for a longer period as per its capacity.
Let’s go back to our previous example. Consider the example of the third phase i.e., knitting. A circular knitting machine is an asset used for knitting. Here is a hypothetical dataset, aggregated from multiple sensors, cleaned, transformed, and ready for consumption.
|Timestamp||Yarn Tension (cN)||Type of Yarn||Speed of Knitting Machine (rpm)||Needle Settings||Ambient Temperature (°C)||Humidity (%)|
Timestamp: The time at which the measurement was taken.
Yarn Tension (cN): The amount of force that is applied to the yarn as it is being knitted.
Type of Yarn: The type of yarn that is being used.
Speed of Knitting Machine (rpm): The rate at which the needles are moving.
Needle Settings: The settings on the knitting machine that control the tension of the yarn.
Ambient Temperature (°C): The temperature of the environment in which the knitting machine is located.
Humidity (%): The humidity of the environment in which the knitting machine is located.
Here is a list of sensors used to track the parameters:
|Timestamp||Real-time clock or GPS receiver|
|Yarn Tension||Load cell or strain gauge|
|Type of Yarn||Optical sensor or chemical sensor|
|Speed of Knitting Machine||Motor speed sensor or needle velocity sensor|
|Needle Settings||Knitting machine control panel or needle sensor|
|Ambient Temperature||Thermistor or infrared sensor|
|Humidity||Capacitive sensor or hygrometer|
Here are some examples to help you understand each feature of the machine monitoring platform:
This is the first step in using a machine monitoring tool. Here are some steps in machine onboarding, i.e., to load the machine details onto the platform. In our case, it is to load the details of the circular knitting machine. Here are some details that can be loaded onto the platform:
Here are some parameters that can be monitored and visualized:
The software can be configured to send out alerts and notifications when these parameters cross their threshold values. Subsequently, this can help ensure safe working conditions and adherence to industry guidelines and regulations. For instance, this can include monitoring parameters such as temperature, noise, and vibration and using the industry-decided numbers as reference/thresholds for comparisons. When machines work under optimal conditions, the likelihood of them developing problems reduces, and in turn, improves their lifespan.
The machine monitoring solution can use machine learning to predict when the machine is likely to fail. This information can be used to schedule maintenance before the machine fails, which can help to prevent downtime. It can also provide recommendations for improving the performance of the machine. This can help to ensure that the machine is operating at its best and that it is producing high-quality fabric.
For example, here are some other combinations of parameters that could lead to failure:
With an increase in the size of the dataset and the number of parameters, training a machine learning model on the historical data (refer to table 1) can help identify the set of parameters and their values that result in machine failure. The model can then take recent data as input to check if the machine conditions are appropriate. If there happens to be a set of rows that contain values such that the probability of failure is high (as predicted by the ML algorithm), there is a notification sent out to the maintenance team. The maintenance team can then assess for potential issues and make necessary repairs. This works on the principle of a proactive instead of a reactive maintenance approach.
The data collected by the machine monitoring solution can be used to create a service catalog. This catalog can list the services that are available for the machine, such as preventive maintenance, repairs, and upgrades. This can then be stored in a repository for future reference and for scheduling maintenance activities.
All the above steps carried out effectively with the assistance of asset management software can help in improving an asset’s lifespan.
We have seen how asset management software tools, as part of the asset maintenance management strategy, can make the process of machine maintenance and monitoring more convenient and efficient when compared to traditional approaches. And with the numerous features that the solutions offer, companies can improve their workflow, save money on repairs and maintenance, and deliver quality products on time to meet dynamic customer demands.
iDataOps , an asset management software tool by Rawcubes, provides easy integration with manufacturing equipment to make a truly smart manufacturing facility. A highly configurable platform that combines development and machine monitoring operations to support your business objectives related to your machine's safety, reliability, and sustainability initiatives. iDataOps aligns data to machine specifications to monitor the performance of the machinery, identify problems or abnormalities, and make predictive decisions. It offers a fully integrated OEE control center to unveil each machine’s availability, performance, and quality metrics.
Take a proactive approach to machine maintenance with iDataOps predictive maintenance from Rawcubes. Don't wait for breakdowns to happen; be proactive instead of reactive. Contact Rawcubes today to discover how iDataOps can give your organization the advantage it needs.