What do all the “buzzwords” CBM, PdM, IoT, AI and ML mean for your production?
Predictive maintenance is a production maintenance concept, where condition monitoring techniques are being used for predicting future condition and maintenance operations on a production machine. By measuring vibrations from machines, we can monitor and detect if a fault is developing, and what the necessary actions will be to plan out the optimal maintenance procedure for the specific situation.
All bodies possessing mass and elasticity are capable of vibration. Vibration is one of the earliest stages for detecting faults and condition deviations in production machines. When measuring vibrations, you want to establish a baseline of data from the environment in which the machine is positioned. From that baseline, several tools can be used to visualize data and to predict machine condition.
A short run-down memory lane – from run-to-failure to true predictive analytics
Condition-based maintenance falls into the category of predictive maintenance, which is the newest and most effective method of making the production facility more reliable. Older approaches to production maintenance are run-to-failure and preventive maintenance.
Run-to-failure is a method of letting the machinery be in operational mode until the machine breaks down. This is, of course, an expensive maintenance program. Therefore, preventive maintenance was developed in two shapes, time-based or usage-based.
The concept of preventive maintenance to perform maintenance on the machinery regularly to lessen the likelihood of it failing. When preventive maintenance is performed as a time-based strategy, maintenance is performed at scheduled time intervals. This concept of maintenance has been updated, as more effective and reliable methods can be implemented now.
Here enters the predictive maintenance program, in which the goal is to predict when maintenance is needed and plan out the maintenance in detail, resulting in the shortest possible downtime. Condition-based maintenance can be implemented in this case, as this concept builds on the data from the vibrations of machines. The vibrations are the earliest stage for detecting faults or condition deviations, which means we can trigger maintenance alarms way before a breakdown happens and plan out the procedure.
Condition monitoring? Yes – monitoring the condition, but how and why now?
Monitoring is defined as an: “activity performed either manually or automatically, intended to observe the actual state of a machine or item“.
Thus far, it has been difficult to achieve effectiveness in maintenance operations because there is no information visible during the product usage period. However, emerging technologies are now expected to come into rapid use to gather and monitor the status data of products during their usage period. New technologies have accelerated growth in the area of CBM by enabling network bandwidth, data collection, data analysis, and decision support capabilities.
Hexastate relies on the newest technology within wireless sensor communication, 4G data transfer, artificial intelligence, machine learning, and deep neural networks. By using artificial intelligence, we can calculate, detect and track a machine’s specific fault frequencies, enabling us to make precise and in-depth vibration analysis on the machine. This provides us with the ability to catch vibration patterns that suggest any early-stage evolving faults or wear on the machines. The latest developments within deep neural networks also enable us to structure models for predicting future developments in the health of the machine way ahead of time.
Maintenance performance measurement
Machine health monitoring as a performance measurement
Generally, CBM can be treated as a method to reduce the uncertainty of maintenance activities and is conducted according to the requirements indicated by the equipment’s condition. CBM can identify and solve problems in advance before machinery damage occurs, any product damage can have serious consequences. In this respect, CBM is a very attractive method for an industry operating the high-valued asset.
In CBM, the lifetime of the equipment is monitored through its operating condition, which can be measured based on monitoring parameters such as vibration, temperature, lubricating oil, contaminants, and noise levels. CBM is a tool for machine health monitoring, asset lifetime utilization, and production optimization recommending maintenance actions (decisions) based on the information collected from the sensors.
Maintenance performance measurement can be defined as the multidisciplinary process of measuring and justifying the value created by maintenance investment and taking care of the organization’s stakeholder’s requirements viewed strategically from the overall business perspective. Maintenance performance measurement is required for measuring the value created by maintenance to justify the investment made and revise the resource allocation and to care for customers, health, safety, and environmental issues while adapting to new trends in operation and maintenance strategy and organizational structural changes.
As maintenance spending constitutes a large share of the operating budget for organizations with heavy investments in machinery and equipment, tracking the performance of maintenance operations in such organizations should be a key management issue. Another reason for linking the measurements to the organization’s strategy is the influence of the applied performance measurements on employee behaviours.
Why CBM makes sense and can save you not only money but also make complex maintenance simple
CBM can provide the following:
CBM can be performed while machinery is working
Replicate Human resources
Decrease costs of failures
Improves equipment reliability
Minimizes unscheduled downtime and catastrophic failures
Less time spent on maintenance
Fewer overall costs by scheduling the activities
Minimizes requirement for spare parts
Optimizes maintenance intervals
Improves worker safety
Reduces the chances of critical damage to the system
CAGR for CBM growth past 5 years
There has been an exceptional growth, but we are yet to see the real value
The Machine Condition Monitoring market size attained a value of $3 billion in 2018. Furthermore, the demand is rising at a CAGR of 39% during the forecast period 2016-2022. The report covers the Machine Condition Monitoring market size by type and applications, Machine Condition Monitoring market share by top 5 companies and the market share by start-ups during the forecast period. The global market size is predicted to grow up to 23 billion US dollars by 2026.
Another key factor enabler for this growth has been the great advancement in hardware technology at a lower cost and the many possibilities of using analytics to predict using large datasets and real-time data streams.
Huge corporations use condition-based maintenance not just as a process to improve but also for generating money revenue.
Implementation of advanced maintenance technologies is the key to sustainable production
By simultaneously reducing operations and maintenance costs and increasing the performance and reliability of production equipment, an effective and efficient maintenance strategy can provide manufacturing companies with a competitive production system. Quite a few proposals for strategic approaches describe how to formulate maintenance plans to support the company’s strategic goals.
CBM can play a key role in providing persons responsible for management and maintenance with correct and reliable data to prevent unplanned production stoppages. There is a lack of cost models to investigate the potential savings from implementing CBM in the manufacturing industry. The practical implementation of advanced maintenance technologies, such as CBM, in the manufacturing industry, is scarce.
Most studies address the technical aspects of CBM, with less attention devoted to organizational aspects. An appropriate approach to the implementation of CBM is needed. Some of the necessary factors to consider when deciding whether to implement CBM are training, management direction, technology selection, user commitment, and user competence. Although CBM can be very useful within the operations of manufacturing companies only a few manufacturing companies are working on developing strategic maintenance
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