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1.Analyze
2.Predict
3.Many More Etc...
Project- Predictive maintenance (PdM) (A Microsoft Azure case study- https://notebooks.azure.com/Microsoft/projects/PredictiveMaintenance/html/Predictive%20Maintenance%20Modeling%20Guide%20Python%203%20Notebook.ipynb)
A major problem faced by businesses in asset-heavy industries such as manufacturing is the significant costs that are associated with delays in the production process due to mechanical problems. Most of these businesses are interested in predicting these problems in advance so that they can proactively prevent the problems before they occur which will reduce the costly impact caused by downtime.
The problem is formatted as a multi-class classification problem and a machine learning algorithm is used to create the predictive model that learns from historical data collected from machines.
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Reactive maintenance (also known as breakdown maintenance) refers to repairs that are done when equipment has already broken down, in order to restore the equipment to its normal operating condition.
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Preventive maintenance (or preventative maintenance) is maintenance that is regularly performed on a piece of equipment to lessen the likelihood of it failing. It is performed while the equipment is still working so that it does not break down unexpectedly.
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Predictive maintenance is a proactive maintenance strategy that tries to predict when a piece of equipment might fail so that maintenance work can be performed just before that happens.
The goal of predictive maintenance is to optimize the balance between corrective and preventative maintenance, by enabling just in time replacement of components. This approach only replaces those components when they are close to a failure.
Any predictive maintenance use case begin with understanding the business problem and defining the objective. Following are some of the common questions which are asked in Predictive Maintenance
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What failure could occur within next 24 hours for my vehicle?
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What is the remaining useful life (RUL) of a vehicle’s component?
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What are the causes of failure of my vehicle and what action should be taken to fix it?
Businesses face high operational risk due to unexpected failures and have limited insight into the root cause of problems in complex systems. Some of the key business questions are:
- Detect anomalies in equipment or system performance or functionality.
- Predict whether an asset may fail in the near future.
- Estimate the remaining useful life of an asset.
- Identify the main causes of failure of an asset.
- Identify what maintenance actions need to be done, by when, on an asset.
- Reduce operational risk of mission critical equipment.
- Increase rate of return on assets by predicting failures before they occur.
- Control cost of maintenance by enabling just-in-time maintenance operations.
- Lower customer attrition, improve brand image, and lost sales.
- Lower inventory costs by reducing inventory levels by predicting the reorder point.
- Discover patterns connected to various maintenance problems.
- Provide KPIs (key performance indicators) such as health scores for asset conditions.
- Estimate remaining lifespan of assets.
- Recommend timely maintenance activities.
- Enable just in time inventory by estimating order dates for replacement of parts.
These goal statements are the starting points for:
- data scientists to analyze and solve specific predictive problems.
To train a Machine Learning model to predict the probability that a machine will fail 24 hours prior to the breakdown, due to a certain component failure (component 1, 2, 3, or 4)
The relevant data sources for predictive maintenance include, but are not limited to:
- Machine operating conditions & usage (telemetry data)
- Error History/ logs
- Failure history
- Equipment metadata
- Maintenance/repair history (components)
Sensor based (or other) streaming data of the equipment in operation is an important data source. A key assumption in PdM is that a machine's health status degrades over time during its routine operation. The data is expected to contain time-varying features that capture this aging pattern, and any anomalies that leads to degradation. The temporal aspect of the data is required for the algorithm to learn the failure and non-failure patterns over time. Based on these data points, the algorithm learns to predict how many more units of time a machine can continue to work before it fails.
These are non-breaking errors thrown while the machine is still operational and do not constitute as failures.
Failure events are rare in PdM applications. However, when building prediction models, the algorithm needs to learn about a component's normal operational pattern, as well as its failure patterns. So the training data should contain sufficient number of examples from both categories. Maintenance records and parts replacement history are good sources to find failure events. With the help of some domain knowledge, anomalies in the training data can also be defined as failures.
metadata about the equipment. Examples are the equipment make, model, manufactured date, start date of service, location of the system, and other technical specifications.
Maintenance history of an asset contains details about components replaced, repair activities performed etc. These events record degradation patterns. Absence of this crucial information in the training data can lead to misleading model results. Failure history can also be found within maintenance history as special error codes, or order dates for parts. Additional data sources that influence failure patterns should be investigated and provided by domain experts.
Given the above data sources, the two main data types observed in PdM domain are:
Temporal data: Operational telemetry, machine conditions, work order types, priority codes that will have timestamps at the time of recording. Failure, maintenance/repair, and usage history will also have timestamps associated with each event.
Static data:* Machine features and operator features in general are static since they describe the technical specifications of machines or operator attributes. If these features could change over time, they should also have timestamps associated with them.
The first data source is the telemetry time-series data which consists of voltage, rotation, pressure, and vibration measurements collected from 100 machines in real time averaged over every hour collected during the year 2015. Below, we display the first 10 records in the dataset. A summary of the whole dataset is also provided.
Mean voltage
Machine-wise maximum pressure
Check for auto correlation
The second major data source is the error logs. These are non-breaking errors thrown while the machine is still operational and do not constitute as failures. The error date and times are rounded to the closest hour since the telemetry data is collected at an hourly rate.
These are the records of component replacements due to failures. Each record has a date and time, machine ID, and failed component type.
This data set includes some information about the machines: model type and age (years in service).
These are the scheduled and unscheduled maintenance records which correspond to both regular inspection of components as well as failures. A record is generated if a component is replaced during the scheduled inspection or replaced due to a breakdown. The records that are created due to breakdowns will be called failures
