Algorithms for Monitoring and Predictive Maintenance
As we said, if you can predict when a machine breakdown will occur, you can schedule maintenance right in advance. The good news is that predictive maintenance lets you estimate time to failure. It also pinpoints problems in your complex machinery and helps you identify what parts need to be fixed. By diagnosing or predicting failures, you can plan maintenance in advance, manage inventory more efficiently, reduce downtime, and increase productivity.
Typically, a machine learning approach is commonly used to define the current state of a system and to predict its future state. It uses data to predict when a machine will break down before it actually breaks down. The development, management and governance of machine learning models is essential for the success of this type of maintenance.
Artificial intelligence is able to process large amounts of complex data in a very short time, which is why many decisions are now delegated to machines. Extensive data is collected and processed (in real-time) along the value chain, which can then be used to analyze the current situation and redefine the desired situation. In this context, it is important for companies to define which data is relevant, in relation to the technology used. The next step is to find and integrate the appropriate measurement tool to capture the values, before defining a model or algorithm suitable for data collection and processing. In this context, it is also important to understand that all stages of the value chain influence each other, which is why an isolated approach is not useful in most cases.
Developing a predictive maintenance machine learning model is not an easy task. It requires a lot of time, effort and resources. A possible high-level strategy could be broken up into these five phases:
- Define the problem — identify the need for predictive maintenance and define the specific problem to be solved by the predictive maintenance machine learning model.
- Collect data — collect relevant data from different sources that can help in solving the problem.
- Prepare data — prepare all of the collected data into a usable format.
- Analyze data — analyze all of the prepared data to identify patterns and relationships that may be useful in predicting future events or occurrences.
- Develop algorithm or machine learning models — develop algorithms or machine learning models based on analyses conducted in step 4 above.
This sort of algorithm could be a good start to move towards the development of an intelligent factory in which each machine can be interconnected to the information system and exchanged with the other machines of the factory and where artificial intelligence can help, not just the maintenance, but the entire production to become predictive.
Therefore, once we have defined the specific problem to be solved by the predictive approach, we need to set up data acquisition and storage, which is done by installing sensors on the machinery. The next step is to set up an optimization function that will be used for training and testing the model. Finally, you need to train and test your predictive maintenance machine learning model using samples from your data set.
Workflow for ML model development
To collect a large set of sensor data representing healthy and faulty operations. You also want to make sure that you collect this data under different operating conditions. For example, the same machine may run in two different places, one in North Europe and one in China. The two machines may operate in different operating conditions, so that, despite having the same type of machine, one may fail sooner than the other.
Having all the data collected will give you the possibility to develop a robust algorithm to detect faults more effectively. In some cases, you may not have enough data to represent a healthy and faulty machine, so what you can do is build a mathematical model of the machine and estimate its parameters based on the sensor data. Since the model represents a simulation of the real-world system, in order to model the reliability of the machine, one needs to simulate it with different fault states under different operating conditions. This can be done also by using a failure data generator.
Therefore, we have generated the supplementary data to integrate to that of the sensor; at this point, you can use a combination of both to develop your algorithm. However, once we have the “raw” dataset, it makes sense to remove the outliers and perform a data-cleaning task, for example by filtering out the noise or other useless data. This stage consists of an additional pre-processing step that is often required to reveal additional information that may not be evident in the original form of the data. The data pre-processing stage in addition to the removal of outliers and missing values also consists of advanced signal processing techniques such as short-time Fourier transforms and order domain transformations. For example, it might be useful to convert data from the time domain to the frequency domain to capture useful features condition indicators that are functional to the ability of the ML model to distinguish healthy from faulty conditions.
The following macro stage is the identification of conditions, which allows us to train machine learning models that is the process at the heart of the predictive maintenance algorithm.
As part of this stage, it is possible to detect anomalies, to train a classifier to identify different types of faults, to gain insights into what part of the machine, equipment or component need to be repaired, and to predict the trend the machine is likely to follow on its path to transition between the states.
Being able to develop a model that captures the relationship between the extracted characteristics and the degradation path of the machine or one of its components, will help you to estimate how much time is left to failure and when you should schedule maintenance.
Then, once the predictive model has been developed, it must be operationalized, distributed on the cloud or on the edge devices (edge computing).
Model operationalising isn’t a trivial process and often represents a challenge for many companies. It is estimated that every single day, companies spend millions of dollars on data scientists, software engineers and artificial intelligence applications. Most of these companies do not have a strategy to make the most of their investment and therefore fail to make a profit. It means that most of the work will never be seen, i.e., the models will either not be put into operation or if delivered (often late) will not render or will not produce the desired results.
So how can we be sure that the models developed for our purposes can effectively perform within established thresholds? How can we manage the entire model lifecycle by setting up a strategy to monitor all models in production and deliver them quickly to achieve business purposes?
ModelOps: A key capability to scale and govern AI initiatives
ModelOps is undoubtedly an organizational capability that enables large enterprises to scale and govern their AI initiatives. More precisely, Enterprise ModelOps proves to be essential for large, complex enterprises with broad plans to leverage models across their operations, while ModelOps is required in some form by any organization.
DevOps-inspired ModelOps practices ensure regulatory compliance, security and manageability, allowing for continuous delivery as well as smooth and efficient development and deployment of models at scale. ModelOps is important for predictive analytics at scale.
For this reason by setting up a ModelOps process for your machine learning models in order to have in place a predictive maintenance process, your factory can benefit from a full end-to-end AI/ML lifecycle that optimizes your data and AI investments. This practice will allow you to streamline and accelerate data collection and management, model development, model validation, and model deployment. ModelOps is indeed a key capability essential to improve, simplify and automate AI/ML operations and lifecycle management.
ModelOps platforms such as ModelOp Center automate all aspects of model operations, regardless of the model type, how it is developed, or where it is run. The benefits of automation are numerous, such as improving decision-making to achieve faster lead times and maximize production rates, reducing personnel costs, and capturing data that would otherwise be lost through manual methods.
An example of an Enterprise ModelOps Platform can be seen in the following schema.
ModelOp Center v3.0 is the leading ModelOps platform designed to address the challenges that enterprises face in scaling and governing AI programs. ModelOp Center 3.0 introduce three new targeted solutions to address the specific concerns of leaders across the organization.
- Executive Visibility for AI solution gives senior executives across the organization a view into where are all AI models running across the enterprise, what business value are they actually driving and are there any operational risk concerns.
- Model Risk Industrialization is a solution designed for risk and compliance teams, to help them to cope with the explosion of the number of models and the actual resource shortage that is occurring for model validators globally.
- AI Orchestration is designed for IT teams to help them to provide consistency standardization and how they onboard and operate models regardless of where models are created. The technologies used in the data science platforms are where those models need to run on-prem in the cloud in a vendor environment.
Back to the subject of this article, it is evident that for a maintenance strategy to be effective, based on a predictive approach and made possible by AI, it is essential to establish a ModelOps capability to govern the entire model life cycle, in an agnostic manner regardless of the tools used by the data scientists.
We believe the factory, and the personnel responsible for its operations as well as the operation of the single machine must acquire the necessary skills in order to develop and manage AI for production. Knowing at what stage a potential problem may arise is certainly within the capabilities of the machine manufacturer or whoever operates the machine. The data scientists can certainly develop well-crafted models, but they lack the knowledge of the machinery or the single component in general. It is therefore imperative that even if the model is effective at zero time, it can be managed, trained, and adapted via periodic evaluation and maintenance to keep up with changing expectations, circumstances, production capacities, and business objectives.
Having an Enterprise ModelOps platform in place definitely reduces the cognitive effort, the time, and the cost associated with developing and managing industrial AI / ML models.
Predictive maintenance is one of the most important parts of the maintenance process. It is a big challenge for companies to make sure they are not wasting time and money on equipment that needs repairing. With predictive maintenance, companies can start to predict when equipment will break down and take action before it happens.
We discussed why predictive maintenance is important and what steps you need to follow to develop AI models that can pinpoint problems in your machinery and let you know in advance about a future failure.
Implementing an operational framework for analytic models, including AI and ML, offers a number of benefits. One important aspect of this is that it enables manufacturers to build and deploy analytic models with any tool of their choice, automate ongoing monitoring, management, and governance of those models, regardless of where they are running, and integrate those models with the necessary systems and applications.
Through this agnostic approach, an Enterprise ModelOps platform simplifies the adoption of, for example, digital twins and enables faster deployment of analytic models by automating and orchestrating the entire model production life cycle.