Cable broadband networks are one of the few "last-mile" broadband technologies widely available in the U.S. Unfortunately, they have poor reliability after decades of deployment. The cable industry proposed a framework called Proactive Network Maintenance (PNM) to diagnose the cable networks. However, there is little public knowledge or systematic study on how to use these data to detect and localize cable network problems. Existing tools in the public domain have prohibitive high false-positive rates. In this paper, we propose CableMon, the first public-domain system that applies machine learning techniques to PNM data to improve the reliability of cable broadband networks. CableMon tackles two key challenges faced by cable ISPs: accurately detecting failures, and distinguishing whether a failure occurs within a network or at a subscriber's premise. CableMon uses statistical models to generate features from time series data and uses customer trouble tickets as hints to infer abnormal/failure thresholds for these generated features. Further, CableMon employs an unsupervised learning model to group cable devices sharing similar anomalous patterns and effectively identify impairments that occur inside a cable network and impairments occur at a subscriber's premise, as these two different faults require different types of technical personnel to repair them. We use eight months of PNM data and customer trouble tickets from an ISP and experimental deployment to evaluate CableMon's performance. Our evaluation results show that CableMon can effectively detect and distinguish failures from PNM data and outperforms existing public-domain tools.

Two types of radio frequency (RF) impairments frequently occur in a cable broadband network: impairments that occur inside a cable network and impairments occur at the edge of the broadband network, i.e., in a subscriber's premise. Differentiating these two types of faults is important, as different faults require different types of technical personnel to repair them. Presently, the cable industry lacks publicly available tools to automatically diagnose the type of fault. In this work, we present TelApart, a fault diagnosis system for cable broadband networks. TelApart uses telemetry data collected by the Proactive Network Maintenance (PNM) infrastructure in cable networks to effectively differentiate the type of fault. Integral to TelApart's design is an unsupervised machine learning model that groups cable devices sharing similar anomalous patterns together. We use metrics derived from an ISP's customer trouble tickets to programmatically tune the model's hyper-parameters so that an ISP can deploy TelApart in various conditions without hand-tuning its hyper-parameters. We also address the data challenge that the telemetry data collected by the PNM system contain numerous missing, duplicated, and unaligned data points. Using real-world data contributed by a cable ISP, we show that TelApart can effectively identify different types of faults.

The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants. This work introduces an intelligent city management system that provides a data-driven approach to three use cases: (i) analyze traffic information to reduce the risk of traffic collisions and improve driver and pedestrian safety, (ii) identify when and where energy consumption can be reduced to improve cost savings, and (iii) detect maintenance issues like potholes in the city's roads and sidewalks, as well as the beginning of hazards like floods and fires. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights that enhance security, energy efficiency, and sustainability, while highlighting the potential of AI and IoT-driven solutions for smart city development.

This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance need. We present 31,177 hours of flight data across 28,935 flights, which occur relative to 2,111 unplanned maintenance events clustered into 36 types of maintenance issues. Flights are annotated as before or after maintenance, with some flights occurring on the day of maintenance. Collecting data to evaluate predictive maintenance systems is challenging because it is difficult, dangerous, and unethical to generate data from compromised aircraft. To overcome this, we use the National General Aviation Flight Information Database (NGAFID), which contains flights recorded during regular operation of aircraft, and maintenance logs to construct a part failure dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and consider the probability that the RUL of a part is greater than 2 days. Unlike previous datasets generated with simulations or in laboratory settings, the NGAFID Aviation Maintenance Dataset contains real flight records and maintenance logs from different seasons, weather conditions, pilots, and flight patterns. Additionally, we provide Python code to easily download the dataset and a Colab environment to reproduce our benchmarks on three different models. Our dataset presents a difficult challenge for machine learning researchers and a valuable opportunity to test and develop prognostic health management methods.

The automotive sector has been around for over 100 years and is worth billions. It enables us to get from point A to point B quickly and safely, but it's slowly changing with the times thanks to new technology: artificial intelligence. AI is improving our cars in ways many people didn't think possible just a few years ago, from predicting maintenance issues before they happen to make driving safer for everyone on the road. It's not just a buzzword that will fade into obscurity. It is here, and it's changing the automotive industry.

Throughout each industrial era, the companies best able to embrace change have become the most likely to succeed. In The Future Computed: AI and Manufacturing, Microsoft Senior Director Greg Shaw explores how AI, automation and the internet of things (IoT) present new challenges and opportunities. Here are some of the manufacturers already demonstrating how the latest tech advances are changing the way they work. A collaboration between Thyssenkrupp and Microsoft has led to the development of the elevator industry's first real-time, cloud-based predictive maintenance system. This means an elevator can accurately predict when it is about to fail and summon an engineer, making it far less likely that people could get trapped inside.

In the era of machine learning, blockchain, and the "internet of things" (IoT), Greenprint remains focused on "small data"--monthly energy, water, and waste bills normalized by building and geographic attributes such as square footage, building type, vacancy rates, and heating and cooling degree days. Using Greenprint's shared-data benchmark drawn from these simple data (and managed in the cloud on ULI Greenprint's Measurabl platform), owners can identify which buildings in their portfolio are performing better or worse than the benchmark and spot opportunities for investments in cost-effective technology upgrades, training in best practices (learning from the leaders), and tenant engagement strategies to improve performance. The benchmark also encourages healthy competition among building managers and building portfolio owners, all looking to leverage data to reduce their operating expenses and improve their net operating income (NOI). The Greenprint benchmarking tools are by no means "big data," and this is the way that Greenprint members like it. Over the past nine years, Greenprint members have leveraged these benchmarking data and shared their best practices to cut energy consumption by more than 17 percent and greenhouse gas emissions by more than 20 percent, saving $36.4 million a year in annual energy, water, and waste expenses.

Korean Air's fleet of 139 passenger planes carries millions of passengers across the globe every year. Ensuring each plane is safe and well-maintained is a priority. When Korean Air addressed line maintenance issues with its fleet, its team of 2,000-plus maintenance employees has historically had to pore over troves of maintenance records to find crucial data on everything from how to fix an important plane part to a plane's maintenance history. "Maintenance issues represent a substantial cost to an airline--to the tune of 28 percent of the total operating cost," said Rob Ranieri, Vice President and Partner, Global Industry Offering Leader, Travel and Transportation at IBM. To remedy this tedious and costly practice, Korean Air recently enlisted IBM Watson Explorer to compile and analyze data from various sources, including technician notes, material cost data and in-flight incident history.

From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time – ERP systems with built-in machine learning algorithms are causing a revolution; with the potential to bring greater predictive accuracy to every phase of production, they are aiding organizations in making critical investment decisions. Although research predicts the machine learning market will reach $15.3 billion by 2019, with modern ERPs being so capable, one might wonder how embedding machine learning into your ERP would add value to systems that are already so advanced. A machine learning enabled ERP system can assist service technicians in root cause analysis for maintenance issues. With early indication about potential hazards, you can take timely measures to avert any danger. What's more, you can improve Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level and more easily create revenue streams.

David Poulsen, CutCableToday's IT expert, says connected, or autonomous, vehicles, are attractive because of the technologies that undergird them. "The Internet of Things (IoT) is one part of the equation," Poulsen explains. "The other part is artificial intelligence (AI). It acts as the driver, helping the connected'thing,' which could be a vehicle or inventory system, make smarter decisions." As applied to transportation management, that automated decision-making ability is critical.