Using Predictive Maintenance to Approach Zero Downtime

Transcription

1 SAP Thought Leadership Paper Predictive Maintenance Using Predictive Maintenance to Approach Zero Downtime How Predictive Analytics Makes This Possible

2 Table of Contents 4 Optimizing Machine Maintenance 4 Reactive Versus Predictive Maintenance 5 Predictive Modeling 6 Predictive Maintenance Use Cases 6 Transportation 6 Manufacturing and Production 7 Utilities 8 Medical Equipment 8 Data Centers and Clouds 9 Enabling Predictive Maintenance 9 SAP Predictive Analytics 12 Learn More Paul Pallath, PhD Chief Data Scientist and Director, SAP Advanced Analytics, Dublin 2 / 12

3 With the Internet of Things, machine-to-machine communication, and connected systems, large volumes of high-velocity data streams are now available that capture the behavior of machines in real time. Companies can employ powerful new predictive analytics to perform modeling that enables predictive maintenance for these assets, with the goal of achieving zero downtime. 3 / 12

4 Optimizing Machine Maintenance REACTIVE VERSUS PREDICTIVE MAINTENANCE Until now, machine maintenance has been either reactive (performed when failure occurs) or based on some heuristic (such as servicing the machine every n hours of continuous operation). Parts that fail earlier than expected can be very expensive and time consuming for businesses, because the necessary tools, components, and people may not be available for immediate response resulting in machine downtime. But since such incidents are sporadic, it is not economically feasible for a company to stock all parts and tools and employ resources just to have them at the ready; this results in unnecessary costs. To counter this problem, companies perform scheduled maintenance using either statistical concepts like mean time to failure or other engineering practices based on historical experiences. However, this approach has its own challenge: parts can be replaced or serviced too frequently, which drives up costs unnecessarily. There are also instances where machines fail before the scheduled planned maintenance due to other reasons. With recent advances in the Internet of Things, machine-to-machine communication, and connected systems, it is possible to stream sensor readings from machines so their health can be monitored in real time. Due to the high velocity and volume of data being streamed, it is not humanly possible to check all sensor values. But advances in predictive analytics have made it possible to evaluate the changes in the patterns of the sensor readings and identify the chances of machine failure significantly in advance of the event. It is also possible to identify the probability of other parts failing in due course by analyzing the historical patterns of failures, so that preventive maintenance can be planned. Capturing the changes in patterns in the sensor feeds in real time, and using that information to predict a possible failure and schedule a proactive maintenance strategy, is called predictive maintenance. With the Internet of Things, machine-to-machine communication, and connected systems, sensor readings from machines can be streamed so their health can be monitored in real time. 4 / 12

5 PREDICTIVE MODELING Modeling for accurately predicting the probability of occurrences of rare events like machine failures events that occur very infrequently and are very hard to detect requires robust algorithms. Rare though they are, these events are normally associated with very high costs, so efforts to predict them are necessary. There are two related aspects that make building models for predicting rare events especially difficult. First, due to the very fact that they are rare, the training data does not contain many examples of these events. Given that such events are often complex (for example, the sensor readings at the time of failure may not point to the cause of failure, since the failure could have been the result of a buildup of several other events already reported as past sensor readings), it is often not easy to generalize from the few available examples. Second, depending on how predictive algorithms are configured and what underlying cost functions are, classifiers or models might completely ignore the rare event. Therefore, special attention should be given to the choice of performance metric being used in general and, more important, for cases with unbalanced classes (where one class of responses is disproportionately larger than the opposite class). In addition to this, there are several other complexities: not all parts of a machine fail with the same frequency, the type of failure can vary, and more. Because sensor data is streamed in real time, it can have missing values or garbage values as well. The frequency at which each sensor transmits data can also vary. Hence data preparation and modeling techniques are needed to help prepare the data and build predictive models to address the nature of the problem and capture the necessary information to effectively predict the failure or breakdown of the machine. Accurately predicting the probability of occurrences of rare events like machine failures requires robust algorithms. 5 / 12

6 Predictive Maintenance Use Cases TRANSPORTATION The cost for maintaining an aging fleet of commercial, private, or military vehicles is high, but so is the cost of repairing relatively new vehicles when they break down. There is a great benefit in both cases to reducing the probability of a failure by performing preventive repairs and extending the useful life of the vehicle. Telemetric data coupled with predictive techniques makes this possible today. Telemetric data from thousands of sensors captures the real-time status of the various parts of a vehicle, which can be analyzed for actionable insights to improve the vehicle s safety and performance. For example: Vehicle manufacturers can use predictive analytics on sensor data to find the key factors that influence the fuel efficiency and performance of their vehicles and thus improve the engineering design. Vehicle manufacturers can use sensor data in combination with geolocation to understand the patterns of vehicle failure in different regions and then strategize what services to focus on in each region, and what spares to stock up on to give their customers the best service. Geolocation can be used to indicate to a connected car driver that there are likely potential problems with the car and it should be serviced at the nearest station. Vehicle sellers can analyze sensor data to predict the probability of a breakdown far ahead in time and proactively reach out to their customers to perform maintenance keeping the vehicles running and customer satisfaction high. For fleet owners and public transport departments, it is important to have their vehicles running all the time. Predictive analytics on real-time sensor data streams combined with fleet management systems will make it possible to plan predictive maintenance for fleet vehicles while they are on the road. This will help ensure that the vehicles are maintained at whatever location they are in, avoiding significant downtime and financial losses due to breakdown. MANUFACTURING AND PRODUCTION In manufacturing and production industries employing massive automatic machines, the health of the machines has a significant impact on the quality of the products produced. In addition, any machine downtime can lead to a delay in the production cycle and create a significant bottleneck in the entire production flow, resulting in heavy costs. The profit margins of a company can also be severely dented when machine failures result in substantial downtime. Technological advances have made it possible to collect huge volumes of sensor data and evaluate it to identify anomalies in the readings. For example: Predictive modeling techniques can be employed to predict the failure of a part far ahead in time and proactively replace the parts at the most convenient time in the production schedule to avoid any downtime. Predictive modeling can be used to identify the spare parts and the required tools that need to be stocked for minimizing downtime in manufacturing. 6 / 12

7 During the phases of production, there are often several paths that the product goes through before it is defined as scrap. Predictive modeling can help identify the sequence of paths that result in a high probability of a part being scrapped, and bring this to light far ahead in the production cycle, resulting in significant savings. Testing various mechanical parts during production is critical to ensure that the machines built with them are of the best quality and have optimal performance. For example, engines built for airplanes must be tested rigorously before they can be used. During production of these engines, sensors mounted into them help monitor their hundreds of components and capture the interactions of various engine parts. Predictive modeling can be employed to identify patterns that would result in an engine failure during the production phase itself, thus allowing necessary changes to be made. UTILITIES Power company operators and distributors face significant problems due to transmission losses, meters that have been tampered with, and spikes in demand. The distribution network is spread across large regions, and the network, generators, transformers, turbines, and smart meters are monitored for malfunctions. Any breakdown could result in significant revenue loss for an entire industrial sector or government. Similarly, with the significant advances in instrumentation technology, oil fields, rigs, and refineries have now become more digital. Data generated by thousands of sensors monitoring the functioning of various parts of the oilfield is streamed in real time to a centralized location. Predicting the possibility of a malfunction of any piece of equipment is critical so that corrective action can be taken before a disaster occurs. Predictive modeling can be employed on data that is captured in real time from the sensors connected to the distribution network, for predictive maintenance. In manufacturing, any machine downtime can delay the production cycle and create a significant bottleneck in the entire production flow, resulting in heavy costs. 7 / 12

8 For example: Predictive maintenance on assets such as gas pipes and electrical cables used across the grid can be an efficient and effective way to keep costs down, risks low, and availability high. Predictive modeling can be used to identify the probability of smart meters being tampered with and to plan preventive checks and maintenance of those meters. Sensor data from smart grids can be used for optimizing the grid and for identifying potential failures in the distribution network and scheduling planned preventive maintenance to help ensure zero downtime. MEDICAL EQUIPMENT The breakdown or malfunction of certain diagnostic and lifesaving or life-support equipment in medical and pathology labs can be catastrophic. Sensors within certain types of equipment are used to record human body responses to diagnose critical illness so that appropriate care and medication can be administered. It is therefore critical to monitor this equipment in real time to predict the probability of malfunction or breakdown and schedule preventive maintenance. DATA CENTERS AND CLOUDS Data volumes are growing rapidly, and organizations want to store all this data to mine information for any number of use cases in addition to predictive maintenance. This data is business critical and is typically stored on an elastic infrastructure in data centers. Many organizations are also now moving to the cloud because it is easy to make new services available to their clients, who benefit from the pay-per-use model. It is essential to have the data centers and cloud infrastructure up and running all the time. Unstructured data in the form of logs containing the status of various services running on the infrastructure, available in real time, can be mined for predicting the failure of various services and performing preventive maintenance to achieve zero downtime. 8 / 12

9 Enabling Predictive Maintenance SAP PREDICTIVE ANALYTICS SAP Predictive Analytics software has capabilities that make it a product of choice for building robust models for predicting rare events in the use cases just described. Predictive maintenance data is time series data derived from the readings from thousands of sensors streamed at high velocity at real time. Sensor readings reflect the state of the machine performance at that time. So the time series data must be transformed into a time-stamped population, and new derived features must be defined to find out what changes in the sensor readings over a period of time provide maximum information about the probability of failure of a machine. One of the several ways of modeling data is shown in Figure 1. Various statistical signatures of each sensor are captured for a specific lookback period and associated with a segment of data from the end of that period, to capture information of an event (such as machine failure) during that segment to use in building predictive models. Several such patterns can be generated at a step size equal to the forward segment size. The forward segment size defines the duration of the validity of the forward prediction that is, if the segment size is 10 minutes, then the model built will be able to give the probability of an event occurring in the next 10 minutes. The data manager component in SAP Predictive Analytics enables the user to create ultrawide analytical data sets for a time-stamped population, and automatically extract thousands of derived features that can be used for creating robust models. If the event describes whether the machine has failed or not, then the automated classification component in SAP Predictive Analytics can be used to identify the chances of machine failure based on the ultrawide data set generated by the data manager. On the other hand, if the event is recorded as a continuous number, then the automated regression component in SAP Predictive Analytics can be used to identify the relationship of the sensor with the target. The classification and regression algorithms can identify the key influencers that have the most information to explain the behavior of the event that is being modeled. They also identify the positive, negative, and neutral impact of the range of values of each sensor involved in the event, enabling the user to have an in-depth understanding of the causes leading to the modeled event. With SAP Predictive Analytics, the vast majority of predictive maintenance use cases can be handled automatically by a person with very little data science expertise. 9 / 12

10 Figure 1: Data Preparation for Streams of Sensor Data Raw sensor data stream Time stamp Machine ID Sensor ID Sensor value 18/01/ :32: Sensor /01/ :32: Sensor /01/ :32: Sensor /01/ :32: /01/ :32: Sensor n /01/ :32: Sensor /01/ :32: Sensor /01/ :32: Processed sensor data Machine ID: Time stamp Sensor 1 Sensor 2 Sensor 3... Sensor n 18/01/ :32: /01/ :32: /01/ :32: /01/ :32: Billions of rows low data density 2 Data transformation Look-back period Step size Time series data stream Normal event Abnormal event 3 Ultrawide data sets Millions of rows thousands of columns high information density Time stamp Machine ID Type Num. Sensors Max_Sensor 1 Min_Sensor 1 20/01/ :30 20/01/ : / 12

11 The automated time series in conjunction with an automated regression algorithm helps in problems related to forecasting, such as the remaining time before the machine or its parts break down in other words, the time to next maintenance. This is shown in Figure 2. The automated clustering component enables the user to find robust numbers of clusters in the data and can be used for problems like grouping machines behaving in a similar fashion and subsequent anomalies in the sensor data. Figure 2: Time to Next Maintenance On/off 1 0 Accumulated operating hours Time Next service Now 0 Predicted date 11 / 12

12 All the automated components build several models internally and choose the most robust models based on their predictive power and predictive confidence. Predictive confidence corresponds to the proportion of information contained in the target variable that the explanatory variables are able to explain in other words, the ability of the model to explain the target. Predictive power is the capacity of the model to achieve the same performance when it is applied to a new data set exhibiting the same characteristics as the training data set. This is the generalization ability or robustness of the model. The models can be easily integrated into business processes and can be scheduled for automatic retraining if there is significant deviation in the data or in the model s performance. The vast majority of predictive maintenance use cases can be handled with an automated approach, and the software can be easily used by a person with very little or no data science expertise. For the most complex problems, the expert analytics option in SAP Predictive Analytics provides a workbench where the automated algorithms can be combined with other prebuilt algorithms and with custom algorithms written using the R programming language. LEARN MORE SAP Predictive Analytics software is a powerful solution designed to address several predictive modeling use cases involving high-velocity data streams that capture machine behavior in real time. To find out more about how this software can help your company use predictive maintenance to achieve zero downtime, please contact your SAP representative or visit 12 / 12 Studio SAP 39044enUS (15/07)

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